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Autonomous Navigation of Distributed Spacecraft using Intersatellite Laser Communications

Autonomy for Space Robots: Past, Present, and Future

• Space Robotics (Y Gao, Section Editor)
• Open access
• Published: 19 June 2021
• Volume 2 , pages 251–263, ( 2021 )

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• Issa A.D. Nesnas 1 ,
• Lorraine M. Fesq 2 &
• Richard A. Volpe 3  

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Purpose of Review

The purpose of this review is to highlight space autonomy advances across mission phases, capture the anticipated need for autonomy and associated rationale, assess state of the practice, and share thoughts for future advancements that could lead to a new frontier in space exploration.

Recent Findings

Over the past two decades, several autonomous functions and system-level capabilities have been demonstrated and used in spacecraft operations. In spite of that, spacecraft today remain largely reliant on ground in the loop to assess situations and plan next actions, using pre-scripted command sequences. Advances have been made across mission phases including spacecraft navigation; proximity operations; entry, descent, and landing; surface mobility and manipulation; and data handling. But past successful practices may not be sustainable for future exploration. The ability of ground operators to predict the outcome of their plans seriously diminishes when platforms physically interact with planetary bodies, as has been experienced in two decades of Mars surface operations. This results from uncertainties that arise due to limited knowledge, complex physical interaction with the environment, and limitations of associated models.

Robotics and autonomy are synergistic, wherein robotics provides flexibility, autonomy exercises it to more effectively and robustly explore unknown worlds. Such capabilities can be substantially advanced by leveraging the rapid growth in SmallSats, the relative accessibility of near-Earth objects, and the recent increase in launch opportunities.

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Introduction

The critical role that robotics and autonomous systems can play in enabling the exploration of planetary surfaces has been projected for many decades and was foreseen by a NASA study group on “Robotics and Machine Intelligence” in 1980 led by Carl Sagan [ 1 ]. As of this writing, we are only 2 years away from achieving a continuous robotic presence on Mars for one-quarter century. Orbiters, landers, and rovers have been exploring the Martian surface and subsurface, both at global and local scales, to understand its evolution, topography, climate, geology, and habitability. Robotics has enabled missions to traverse tens of kilometers across the red planet, sample its surface, and place different instruments on numerous targets. However, the planetary exploration of Mars has remained heavily reliant on ground in the loop for its daily operations. The situation is similar for other planetary missions, which are largely operated by a ground crew. A number of technical and programmatic factors play into the degree to which missions can and are able to operate autonomously.

Despite that, autonomy has been used across mission phases including in-space operations , small-body proximity operations , landing , surface contact and interaction , and mobility. Past successful practices may not be sustainable nor scalable for future exploration, which would drive the need for increased autonomy, as we will analyze in this article.

Autonomy for Robotic Spacecraft

Definition and scope.

NASA defines autonomy as “the ability of a system to achieve goals while operating independently of external control” [ 2 ]. In the NASA parlance, a system is the combination of elements that function together to produce the capability that is required to meet a need. These elements include personnel, hardware, software, equipment, facilities, processes, and procedures needed for this purpose [ 3 ]. So, by this definition, an autonomous system may involve a combination of astronauts and machines operating independent of an external entity such as ground control or an orbiting crew. However, in this article, we will only consider autonomy in the context of a robotic spacecraft, where the external actor is ground control. Autonomous robots operated by astronauts in proximity or remotely are outside the scope of this article.

Figure 1 shows the basic abstraction of an autonomous system. With inputs that define the desired objectives or goals, the system perceives its environment and itself (for health monitoring), reasons about them, decides what actions to take, and then executes those actions. The actions affect the system and/or the environment, which impact what would be perceived next. Today’s spacecraft operate largely within the act domain. Perception (except for sensory measurements and rudimentary signal processing) and decision-making are largely performed by personnel on Earth, who also generate commands to be uplinked to the spacecraft to initiate the next set of actions. Autonomous perceptions, decisions, and actions are delegated to the spacecraft in limited cases, when no alternative exists. Onboard autonomy eliminates communication delays, which cause stale state information that ground operators must contend with to close the loop.

The basic abstraction of an autonomous system

Figure 2 shows the basic abstract functions of an autonomous system for situation and self-awareness as well as for reasoning and acting. Situation and self-awareness require sensing and estimation that encompass perception, system-state estimation, model building, hazard assessment, event and trend identification, anomaly detection, and prognosis. Reasoning and acting encompass planning trajectories/motion (mission), planning and managing the usage of resources, and executing activities. It is also responsible for reconciling conflicting information before execution. Some functions, such as learning and coordination, can be employed across a system and among systems. For example, learning can occur in sensing, estimation, reasoning, and/or acting.

Basic functions of an autonomous system

The autonomous functions of a spacecraft are often categorized into two groups: functional level and system level.

Function-Level Autonomy

Functional-level autonomy is typically focused on specific subsystems and implemented with local state machines and control loops, providing a specific subsystem behavior. These domain-specific functions include perception-rich behaviors for in-space operations such as cruise trajectory corrections and proximity operations, getting to a surface via entry , descent , and landing (EDL), and mobility on/over a surface. They also include physical interaction between two assets, such as in-space spacecraft-to-spacecraft docking, grappling, or assembly as well as reasoning within a science instrument to analyze data and make decisions based on goals.

System-Level Autonomy

System-level autonomy reasons across domains: power, thermal communication, guidance, navigation, control, mobility, and manipulation, covering both software and hardware elements. It manages external interactions with ground operators to incorporate goals into current and future activities. It also plans resources and activities (scheduling, prioritizing), executes and monitors activities, and manages the system’s health to handle both nominal and off-nominal situations (fault/failure detection, isolation, diagnosis, prognosis, and repair/response).

An autonomy architecture is a necessary underpinning to properly define, integrate, and orchestrate these functions within a system and support implementations of functions in software, firmware, or hardware. Domain-specific functions have to be architected in a way that allows system-level autonomy to flexibly and consistently manage the various functions within a system, under both nominal and off-nominal situations. Designers have to identify the correct level of abstraction for a given application to define the scope that the system-level autonomy has to reason about. In other words, an integrated autonomous system should not have artificial boundaries not grounded in the fundamentals of the problem to maintain the flexibility that an autonomous system needs. Central to such an architecture is ensuring explicitness of intent, consistency of knowledge in light of faults and failures, completeness (or cognizance of limitations) of the system and behaviors for handling of situations, flexibility in the connectivity of functions to handle failures or degradations, traceability of decisions, robustness and resilience of actions, and cognizance and implications of actions, both in the near term as as well as in the long term. Explorers can or may need to operate for decades, such as the Voyager spacecraft that have been operating since their launch in 1977 [ 4 ].

The Autonomy Continuum

Autonomy that is applied to space systems is a continuum from less autonomy (highly prescriptive tasks and routines) to increasingly autonomous (richer control capabilities onboard) as shown in Fig. 3 . Automation is on the “less autonomy” side of the spectrum, which often follows prescribed actions without establishing full situational awareness onboard and without reasoning onboard about the actions the spacecraft undertakes. Such situational awareness and reasoning are handled by operators on the ground.

Autonomy is a continuum of capabilities

It is important to note that moving more control onboard does not remove science/humans out of the loop. Rather it changes the role of operators. More autonomy allows a spacecraft to be aware of and, in many cases, make decisions about its environment and its health in order to meet its objectives. That does not preclude ground operators from interacting with the spacecraft asynchronously to communicate intent, provide guidance, or interject to the extent necessary and possible.

So, When Do We Really Need Autonomy?

As shown in Fig. 4 , there are two sets of constraints that drive the need for autonomy: (1) operational constraints derived from mission objectives and (2) system/environment constraints based on the spacecraft design and the remoteness and harshness of the environment. For the operational constraints, the use of autonomy is traded against non-autonomous approaches. Based on risk and cost, mission objectives may get adjusted, often leaning toward state-of-the-practice non-autonomous approaches wherever possible. These may include scaling back on the minimum required science, which relaxes requirements on productivity or access to more difficult sites. For the system/environment constraints, autonomy is required (not just desired) if the three conditions below are met. These conditions occur when:

Changes in the environment or spacecraft occur: Examples of changes in the environment include an erupting plume on Enceladus or winds on Titan. Examples of changes in the spacecraft include degradations, faults, or failures.

Changes are not predictable: In a counterexample, during the approach phase to a small body, the relative trajectory, the body’s motion, and the body’s shape are iterated on, carefully managing uncertainties. As such, ground in the loop is often used, obviating the need for autonomy. When changes can be adequately predicted and modeled, ground operators use this information to prescribe the set of actions and hence do not require autonomy.

Required response time is shorter than next communication cycle: This condition occurs when the spacecraft has to react to a situation before its next communication cycle with the ground. This was the case during the final stage of OSIRIS-REx’s touch-and-go sampling that employed autonomy [ 5 ]. Similarly, if a rover is on steep slope and is slipping, there is no time to communicate with ground operators before reacting to the situation.

The need for autonomy (left) and what autonomy enables (right)

Skeptics have argued that due to the risks of deep-space exploration and the rarity of these historic opportunities, it is unlikely that future missions would entrust such critical decisions to an onboard autonomous system. Past missions, such as Cassini , have achieved great success with ground in the loop. Some would argue that the situations that arise are too numerous, complex, and unknown for a machine to reason about, and they are too risky not to rely on a broad range of ground expertise and resources (e.g., compute power). With time delays of only single-digit hours across our solar system, engaging ground operators for these remote missions would both be viable and sensible. This is a reasonable argument and one that has underscored the state of the practice, but it hinges on two assumptions that may no longer hold true in the future: (1) our ability to predict outcomes to a reasonable degree and (2) the availability of adequate resources (e.g., power, time, communication bandwidth, line-of-sight) to have ground in lock-step with the decision-making loop. Consider a Europa surface mission duration, which would be constrained by thermal and power considerations [ 6 ]. A mission that needs to collect and transfer samples from an unknown surface in a limited time requires a degree of autonomy to successfully handle its potentially unpredictable surface interaction. A different example is the Intrepid lunar mission concept [ 7 ]. With its proximity to Earth, the Moon is a destination that typically would not justify the need for autonomy. However, when you consider the proposed mission objectives that require traversing 1800 km with hundreds of instrument placement across six distinct geological regions in 4 years, such a mission would have to rely on a large degree of autonomy to be successful. A detailed study of this mission concept has shown that communication availability (through DSN as well as projected bandwidths through a commercial communication infrastructure within this decade) would drive the need for largely autonomous operations, with intermittent ground interventions to direct intent and handle anomalies that cannot be resolved onboard.

Figure 5 compares trends of past, present, and anticipated future needs. Spanning this time frame, we note a growth in the diversity of forms of robotic explorers: starting with spacecraft on flyby and orbiting missions, to today’s missions that include landers [ 8 ], subsurface probes [ 9 ], rovers [ 10 ], and most recently rotorcraft [ 11 , 12 ]. Future missions could include even more diverse forms of explorers such as balloons or dirigibles [ 13 ], hoppers [ 14 , 15 ], walkers [ 16 , 17 ], rappelers [ 18 ], deep melt probes [ 19 ], mother-daughter platforms, and multi-craft missions [ 20 ]. Whereas earlier planetary exploration missions operated in vacuum away from planetary bodies, today’s missions are operating on or into a wide range of planetary surfaces with different properties [ 9 , 21 ]. The surface and subsurface of such bodies are not well characterized. Scooping and probing in these bodies have proved more challenging than had been anticipated [ 22 , 23 , 24 ].

Evolution of deep-space exploration drives the need for greater flexibility

Also, today’s missions have spacecraft with much richer sensing and perception than prior missions. Landing systems are equipped with high-resolution cameras and LIDARs for terrain-relative navigation and hazard assessment [ 25 , 26 ]. The Mars rovers carry tens of visual cameras, often in stereoscopic configurations, to establish situational awareness for ground operations. Interactions with the environment, whether for manipulation, probing, sampling, or mobility, are governed by empirical models that are sensitive to terrain heterogeneity [ 27 ].

All this to say that current and future robots are operating and interacting in largely unknown environments, with perception-rich systems but limited physical models that govern their interaction with the environment [ 28 ]. While past missions have been successful by relying on their ability to predict the execution of activities days or even weeks in advance based on orbital dynamics, current and future in situ missions will continue to be challenged in their ability to predict outcomes of actions given the complex and incomplete models that govern that dynamic.

In summary, future robots will likely be operating in largely unknown and diverse environments, with perception-rich systems that experience large uncertainties, causing the ability of ground operators to predict behavior to be severely limited. As such, to be successful, we argue that future robotic systems should be more flexible and more autonomous to handle situations that arise. Given this rising complexity, made worse by communication and power constraints, the desire to push the boundaries of exploration will drive the need for more autonomy [ 29 ••].

State of the Practice, Challenges, and Benefits

State of the practice.

Despite the successful demonstrations and uses of autonomous capabilities on a range of spacecraft, from in-space to surface missions, autonomy only gets adopted when it is absolutely necessary to achieve the mission objectives, such as during EDL on Mars. It is often the case that enhanced productivity does not fare well against a perceived increase in risk. The current posture vis-à-vis the adoption of autonomy is understandable, given the large costs and the rare opportunities afforded to explore such bodies.

Figure 6 captures an abstract comparison of state of the practice in spacecraft autonomy (see “Autonomous Robotic Capabilities in Past and Current Space Missions” for a summary of advances) relative to a possible future autonomous spacecraft. To date, autonomous capabilities have been deployed either within limited operational windows (green bars) or in scenarios that were carefully modeled a priori and pre-scripted for a deterministic or relatively well-anticipated outcome. Prior to handing control to the autonomous spacecraft (engagement), ground operators use telemetry, physics-based models, and expert judgement to establish situational awareness, even though the spacecraft telemetry may be stale given communication delays and ground-planning durations. As such, these missions have the benefit of ground-in-the-loop engagement to assess situations pre- and post-deployment. This awareness is also used to constrain the autonomous behaviors before engagement. For example, rover navigation is sometimes aided by ground operators’ assessments of safe keep-in-zones and unsafe keep-out-zones from orbital data to constrain the rover’s actions to remain within a relatively safe corridor [ 30 •]. This is akin to a parent guarding their child during their first steps. Spacecraft fault protection is often disabled or restricted during critical events, lest it results in an unexpected course of action that could put the mission at risk [ 31 ]. Such state-of-the-practice actions are adopted from successful past missions since they have proven reliable over the years.

A perspective on state of the practice and a vision of the future

Therefore, today’s sequence-driven operations heavily rely on human cognizance to establish situational awareness and command the spacecraft. For most space missions, in particular flyby and orbital, operators are able to plan activities days or weeks in advance because the physics of the problem are well understood and reasonably modeled. Spacecraft that rely on orbital dynamics, such as Galileo and Cassini , were able to execute pre-programmed sequences and manage resources for up to 14 days autonomously (between two scheduled Deep Space Network antenna passes) with unattended operations [ 32 ]. This also includes recognition of on-board failures and execution of appropriate recovery operations. Contrasting this with surface operations of the Mars rovers or operations in the vicinity of a small body, the ability to predict outcomes of activities significantly diminishes to short-time horizons. In situ robotic spacecraft operating in poorly constrained or dynamic environments (e.g., Venus, Mars, or Titan) have to rely on local assessments of the situation at hand in order to take action.

Future Challenges

The adoption of increasing levels of autonomy faces both technical and non-technical challenges.

Practices that resulted in past successes do not necessarily imply their suitability for future missions, where the spacecraft has to operate in regimes of much higher uncertainties and poorly modeled physics: namely, the physical interaction with unknown and never-before-visited surfaces. For example, Mars missions have had the benefit of a priori knowledge of the Martian atmosphere and surface from prior missions, which were heavily used in developing models for testing the autonomous capabilities of entry, descent, and landing [ 33 ] as well as surface navigation [ 34 , 35 ]. Future missions that would explore unknown worlds need to handle the large uncertainties and the limited a priori knowledge of the environment in which they must operate. The topographies of such surfaces may be unknown at the scale of the platform (e.g., as of this writing, the best available resolution of Europa’s surface is at 6 m/pixel, well below what is necessary for the scale of a future landing platform [ 36 ]), and the material properties are often poorly constrained [ 27 ] (e.g., the Curiosity rover encountered uncharacteristic terrain with high sinkage in Hidden Valley [ 37 ]) or exhibit unexpected behavior (e.g., the Spirit rover unexpectedly broke through a duricrust layer and became embedded and immobilized in unconsolidated fines [ 38 ], the Phoenix scooped sample appeared to congeal, possibly as a result of solar radiation impinging on the sample, and cause the sample to stick to the scoop during delivery to the instruments [ 8 ]). Future missions would have to operate for extended periods of time depending on available communications and in light of faults and failures that they will inevitably experience during their treacherous exploration of planetary bodies. Being able to adapt and learn how to operate in these harsh environments is becoming an important aspect of deep-space exploration.

The major technical challenges for autonomy are:

Developing the autonomous functions and associated system functions to the necessary level of maturity,

Having adequate sensing and computing,

Having adequate models and frameworks,

Designing platforms with sufficient flexibility to handle large uncertainties in their interaction with the environment yet meet power, thermal, computation, and communication constraints, and

Having metrics and tools to verify and validate autonomous capabilities [ 39 ].

Among the non-technical barriers are strategic and programmatic challenges associated with the current acceptable risk posture related to mission cost, the necessary reliance on heritage for competed missions to fit within cost caps and to minimize risk, and the limited demand for autonomy from currently formulated missions, which are often conceived based on state of the practice rather than what could become viable. The current paradigm of ground-in-loop-operations does not scale well to operating multiple coordinated spacecraft in large formations [ 20 ]. While the cost of operations would initially be higher for autonomous systems, as the capability gets matured and becomes standard practice, the expected cost would eventually converge to a steady state resulting in potentially substantial reductions in operational cost. Other non-technical challenges are related to changes in people’s roles and a sense of job displacement and loss of control.

Potential Benefits

Autonomy would enable (a) exploring new destinations with unknown or dynamic environments, (b) increasing productivity of in situ operations, (c) increasing robustness and enabling graceful degradation of spacecraft operation, and (d) reducing operations cost and enhancing operability.

Exploration: Autonomy enables greater access to regions on planetary bodies that would have otherwise been inaccessible such as the liquid oceans of icy moons. It also enables new observations, in the presence of unpredictable events that require real-time decision making and execution such as close sampling of Enceladus’ plumes, landing on Europa, and accessing the surfaces of small bodies.

Productivity: Demand for greater productivity in surface operations, greater diversity in science observations, and higher quality observations are expected in the coming years. Autonomy increases productivity by allowing the spacecraft to do more in situ science within the constraints of the mission by monitoring resources and managing activities. It can also assist scientists in identifying and accessing more abundant and more interesting science targets. For example, a study of the Intrepid lunar rover concept concluded that the mission requires a sophisticated degree of autonomy to achieve its long-distance mobility and instrument placements [ 7 ]. A separate study showed that a rover operated by campaign intent rather than sequenced activities had an 80% reduction in sols required to complete a campaign and 267% increase in locations surveyed per week [ 40 ].

Robustness: Spacecraft that are self-aware and are able to self-diagnose and solve problems before they escalate into a larger failure event would increase robustness and decrease mission risk. For example, missions that baseline solar-electric propulsion are at higher risk of missing their targets if a safing event occurs during cruise [ 41 •]. On the Dawn spacecraft, a 4-day period of missed thrust resulted in a 26-day delay to the Ceres orbit [ 42 ].

Cost-effectiveness and operability: Autonomy would simplify operations and therefore reduce operational costs. This frees up spacecraft resources for more science observations, reduces the tedious parts of ground operations, and potentially scales to operating multiple spacecraft.

Autonomous Robotic Capabilities in Past and Current Space Missions

To understand where we are going, it is useful to review in detail where we have been. This section provides an overview of notable prior missions that have contributed to autonomy progress for space applications.

Over the past two decades, the world has witnessed the impact of robotics for the surface exploration of Mars. This includes the first 100 m of Sojourner’s tracks on the red planet, Spirit and Opportunity ’s exploration of dramatically different regions in different hemispheres, and Curiosity’s climbing of Mount Sharp. Complementing Spirit and Opportunity’s discovery of evidence that water once flowed on the Martian surface, the Phoenix mission used its robotic arm to sample water ice deposits in the shallow subsurface of the northern polar region. The Curiosity rover has investigated the Martian geology in more detail compared to its predecessors, using its mobility and manipulator to drill and transfer drill cuttings to its instrument suite. It found complex organic molecules in the Martian regolith and detected seasonal fluctuations of low methane concentrations in its atmosphere. Most recently, the InSight mission used its robotic arm to place two European instruments on the Martian surface: a high-precision seismometer that detected the first ever Marsquake and a heat-flow sensing mole intended to penetrate meters below the surface.

In-Space Robotic Operations

In 1999, the Remote Agent Experiment aboard the Deep Space I mission demonstrated goal-directed operations through onboard planning and execution and model-based fault diagnosis and recovery, operating two separate experiments for 2 days and later for 5 consecutive days [ 43 , 44 ]. The spacecraft demonstrated its ability to respond to high level goals by generating and executing plans on-board the spacecraft, under the watchful eye of model-based fault diagnosis and recovery software. On the same mission, autonomous spacecraft navigation was demonstrated during 3 months of cruise for the 36-month-long mission. It also executed a 30-min autonomous flyby, demonstrating onboard asteroid detection, orbit update, and low-thrust trajectory-correction maneuvers.

In the decade to follow, the Stardust mission demonstrated a similar flyby feat of one asteroid and two comets [ 45 ]. Between 2005 and 2010, the Deep Impact mission conducted an autonomous 2-h terminal guidance of a comet impactor and separately a flyby that tracked two comets [ 46 ]. It demonstrated detecting the target body, updating the relative orbits, and commanding the spacecraft using low-thrust maneuvers. Autonomy has also been used to aid science operations of Earth-orbiting missions such as the Earth-Observing-1 spacecraft, which used onboard feature and cloud detection to retarget subsequent observations for identifying regions of change or of interest [ 47 ]. The IPEX mission used autonomous image acquisition and data processing for downlink [ 48 ]. Most recently, the ASTERIA spacecraft transitioned its commanding from time-based sequences to task networks and demonstrated onboard orbit determination using passive imaging in Low Earth Orbit (LEO) without GPS [ 49 ].

Small-Body Proximity Operations

Operating in proximity of and on small bodies has proven particularly time consuming and challenging. To date, only five missions have attempted to operate for extended periods of time in close proximity to such small bodies: Shoemaker , Rosetta , Hayabusa , Hayabusa2 , and OSIRIS-REx [ 45 , 50 , 49 , 52 ]. Many factors make operating around small bodies particularly challenging: the microgravity of such bodies, debris that can be lofted off their surfaces, their irregular topography and correspondent sharp shadows and occlusions, and their unconstrained surface properties. The difficulties of reaching the surface, collecting samples, and returning these samples stem from uncertainties of the unknown environment and the dynamic interaction with a low-gravity body. The deployment and access to the surface by Hayabusa’s MINERVAs [ 53 ] and Rosetta’s Philae [ 54 ] highlight some of these challenges and together with OSIRIS-REx [ 55 ] underscore our limited knowledge of the surface properties. Because of the uncertainty associated with such knowledge, missions to small bodies typically rely on some degree of autonomy.

Landed Missions

During entry , descent , and landing (EDL) on Mars, command and control can only occur autonomously due to the communication delay and constraints. Landing on Mars is particularly challenging because of its thin atmosphere and the need to decelerate to a near-zero terminal descent velocity with limited fuel, requiring guided entry for deceleration to velocities where parachutes may be used effectively. Uncertainties arise with parachutes due to wind that contributes to a lateral velocity of the descending spacecraft. As a result, in 2004, the Mars Exploration Rover (MER) missions used onboard autonomy [ 56 ] to estimate lateral velocity from descent images and correct it if necessary.

The Chang’e 4 lunar mission carrying the Yutu-2 rover demonstrated high-precision autonomous landing in complex terrain on the lunar far side. The spacecraft used terrain relative navigation as well as hazard assessment and avoidance to land in the absence of radiometric data [ 57 ].

In addition to the Martian and lunar landings, several missions have touched the surfaces of small bodies autonomously. In 2005, the Hayabusa mission demonstrated autonomous terminal descent of the last 50 m toward a near-surface goal for sample collection using laser ranging (at < 100 m) to adjust altitude and attitude [ 58 ]. This capability was also employed on the 2019 Hayabusa2 mission, where the mission used a hybrid ground/onboard terminal-descent with ground controlling the boresight approach while the onboard system controlled the lateral motion for the final 50 m. In 2020, the OSIRIS-REx mission used terrain-relative navigation for its touch-and-go maneuver for sample acquisition. Using a ground-generated shape-model, the spacecraft matched natural features to the image renderings from the generated model to approach the body for its touch-and-go sampling. This segment was executed autonomously but with ground oversight.

Surface missions

Surface contact and interaction is typically needed for instrument placement and sampling operations in scientific exploration. The Mars Exploration Rovers demonstrated autonomous approach and instrument placement on a target selected from several meters away [ 59 , 60 ]. The OSIRIS-REx mission captured samples from the surface of asteroid Bennu using its 3.4-m extended robotic arm in a touch-and-go maneuver that penetrated to a depth of ~50 cm, well beyond the expected depth for the sample capture.

Surface mobility greatly expands the value of a landed mission by enabling contact and interaction with a broader part of the surface. To achieve surface mobility safely, every Mars rover mission hosted some form of autonomous surface navigation. In 1997, the Sojourner rover of the Mars Pathfinder mission demonstrated autonomous obstacle avoidance using laser striping together with a camera to detect untraversable rocks (positive geometric hazards). It then used its bang-bang control of brushed motors to drive and steer to avoid hazards along its path and reach its designated goal. The Mars Exploration Rovers , Spirit and Opportunity, and the Mars Science Laboratory Curiosity rover used a more sophisticated autonomous navigation algorithm, relying on dense stereo mapping from its body- and mast-mounted cameras to assess terrain hazards. Algorithms processed three-dimensional point clouds into a grid map, estimating the slope, height differences, and roughness of the rover’s footprint across each terrain patch [ 34 ].

The Mars 2020 Perseverance rover uses an even more sophisticated algorithm in evaluating a safe traverse path for the rover. It improves the stereo sensing and significantly speeds up its processing using dedicated FPGAs, which will evaluate the tracks of the wheels across the terrain to assess traversability. For path planning, given the computationally intensive calculation of assessing the body-terrain collision when placing a passively articulated rover suspension along the terrain path, a conservative approximation is used to simplify the computation while preserving a safe collision-free path. In addition to the local cost evaluation of the rover’s traverse across the nearby terrain, a global cost is calculated from orbital and previously observed rover data to determine the proper action to take.

The Mars rovers have traversed distances of hundreds of meters autonomously, well beyond what has been visible in imagery used by ground operators (i.e., over the horizon driving). Over one weekend, the Opportunity rover drove 200 m in a multi-sol autonomous driving sequence.

Future Mission Possibilities

Various possible directions for autonomy.

This is only a prelude of what is anticipated. Ongoing mission concept studies and research programs are investigating a range of robotic systems that would explore surfaces of other planetary bodies. These include robotic arms that capture and analyze samples from Europa’s surface. A rotorcraft completed multiple powered flights in the thin Martian atmosphere, through the thin Martian atmosphere, and another large one is being built to explore Titan’s surface, leveraging its thick atmosphere [ 11 , 12 ]. Probes are being studied to reach the oceans of icy worlds, either getting through kilometers of cryogenic ice or by weaving their way through vents and crevasses of Enceladus’ tiger stripes, the site of plumes in the moon’s southern region [ 61 ]. Lunar rovers are being studied to cover thousands of kilometers to explore more disparate regions near the lunar equator [ 7 ] and in the polar regions.

These increasingly rich forms of explorers will require a greater degree of autonomy, in particular, for ocean worlds and remote destinations, where the surfaces of target bodies have never been visited before and where communications and power resources are more constrained than those on the Moon and Mars. While such explorers are likely to be heterogeneous in their form, the foundational elements of autonomy might be shared among such platforms. It is precisely these foundational elements that would need to be advanced to enable robotic systems to effectively conduct their complex missions, in spite of the limited knowledge and large uncertainties of the harsh environments to be explored.

Given the aforementioned challenges, how can we take the next major step in advancing autonomy? To do so, we consider the key gap, which is to reliably operate in situ in a partially known and harsh environment and under inevitable system degradations. This would drive the maturation of the needed function- and system-level autonomy capabilities in an integrated architecture to principally handle a range of conditions. A number of such autonomy challenges have been captured from a NASA Autonomy Workshop by the Science Mission Directorate in 2018 [ 62 ].

Proposed Next Direction: Autonomous Small Body Explorer

One example that could provide an adequately challenging near-term opportunity for advancing robotics and autonomous systems is using an affordable SmallSat (e.g., a spacecraft that is less than 180 kg and has standardized form factors at smaller sizes: e.g., 6U, 12U) to travel to, approach, land, and operate on the surface of a near-Earth object (NEO) autonomously. The SmallSat would be designed to operate using high-level goals from the ground, which will also provide operational oversight. Frequently asked questions and answers for this concept are discussed below.

Why are NEOs compelling for exploration? The exploration of NEOs is important for four thrusts: science, human exploration, in situ resource utilization, and planetary defense. For example, previous missions, Hayabusa and Hayabusa2, were primarily science focused. They largely operated with ground in the loop and their surface operational capabilities were, therefore, limited at the time. We envision autonomous robotic access to the surface of NEOs that would expand on these successes and would have substantial feed-forward potential to enable access to more remote bodies such as comets, asteroids, centaurs, trans-Neptunian bodies, and Kuiper-belt objects. Small bodies are abundant and diverse in their composition and origin and are found across the solar system and out to the Oort Cloud [ 50 ].

Why are NEOs well-suited targets to advance autonomy? NEOs embody many of the challenges that would be representative of even more remote and extreme destinations, while remaining accessible by SmallSats. Given their diversity, their environments are relatively unknown a priori and the interaction of a spacecraft near or onto their surface would be dynamic, given their microgravity. Further, such a mission cannot be easily emulated in a terrestrial analog environment and the utility of simulation is limited by the unknown characteristics of the environment to be encountered.

Why is autonomy enabling for small bodies? Autonomy would enable greater access by reducing operations cost and would scale to allow reaching far more diverse bodies than the current ground-in-the-loop exploration paradigm. With on-board situational awareness, autonomy enables closer flybys, more sophisticated maneuvers during proximity operations, and safe landing and relocating on the surface. Operating near, on, or inside these bodies requires autonomy because of their largely unknown, highly rugged topographies and because of the dynamic nature of the interaction between the spacecraft and the body. Missions such as Hayabusa and Hayabusa2 that deployed surface assets largely operated with ground in the loop and their surface operational capabilities were limited at the time.

Approaching, landing, and reaching designated targets on a NEO requires technical advances in computer vision, machine learning, small spacecraft, and surface mobility. An autonomous mission with limited a priori knowledge of the body would establish, during approach, estimates of the body’s rotation rate, rotation axis, shape, gravity model, local surface topography, and safe landing sites using onboard sensing, computing, and algorithms. An onboard system has the advantage of higher image acquisition rates that would be advantageous for the computer vision algorithms and would result in a much-reduced operations team, when compared to ground operations that would be subject to limited communication bandwidth. Machine learning would be able to encode complex models and handle large uncertainties, such as identifying and tracking body-surface landmarks across large scale changes and varying lighting conditions during tens of thousands of kilometers of approach. Furthermore, machine learning would handle complex dynamic interactions with the surface, whose geo-physical properties are not known a priori, to enable effective mobility and manipulation. Such an autonomous capability, once established, would be more broadly applicable to planetary bodies with unknown motions/rotations, topographies, and atmospheric conditions, should the latter exist.

Such a scenario has clear success metrics for each stage of increasing difficulty. During cruise, trajectory correction maneuvers would guide the craft to the approach point, when the target becomes detectable (subpixel size but appears as point-spread function) in the camera’s narrow field of view. The approach is a particularly challenging phase whose success is reaching a hover point at a safe distance, having established the body parameters (trajectory, rotation, and shape). The subsequent phase would involve the successful landing site selection, guidance, and safe landing. For a NEO, such a maneuver would have the flexibility of an abort and retry given the microgravity of the body. Mobility on the surface to target locations and the ability to manipulate the shallow regolith surface to acquire measurement would constitute the last phase and success metric. While all operations would be autonomously executed, these would be responding to goals set by scientists and ground operators and the performance of the craft would be continually monitored by ground operators as the capability is proven. The last success metric is the download of key information to trace and analyze the onboard decisions that the spacecraft has been making all along.

Results from an earlier analysis of both accessibility and feasibility of such a scenario showed promise [ 63 , 64 ]. To simplify access to the surface, we design the spacecraft to be able to self-right and operate from any stable state, where it can hop and tumble similar to what has been demonstrated in parabolic flight, but possibly using cold gas thrusters in lieu of reaction wheels [ 15 ]. Once on the surface, we assume a limited lifetime to reduce constraints associated with large deployable solar panels. In addition to guiding the spacecraft during landing, micro-thrusters can also be used to relocate the platform to different sites on the body. Miniaturized manipulators developed for CubeSats could enable such a platform to manipulate the surface for sampling and other measurements [ 65 ]. Such a scenario could be extended to multi-spacecraft missions.

In addition to the functions that would have to be matured, this scenario would drive the development of an architecture that integrates function- and system-level elements to enable cross-domain models to interact at the proper fidelity levels to execute a full and adequately challenging mission, but with provisions for ground oversight and retries. The relatively low cost of such a technology demonstration would allow a more aggressive risk posture to substantially advance autonomous robotic capabilities. By making the architecture and algorithms widely available, the bar of entry for universities will be lowered, allowing greater opportunities to send SmallSat missions to diverse NEOs.

Concluding Thoughts

In this paper, we have provided a broad overview of autonomy advances for robotic spacecraft, summarized state of the practice, identified challenges, and shared potential benefits of greater adoption. We presented an argument for why the state of the practice of sequence-driven, ground-controlled operations that led to numerous successful missions would not be a well-suited paradigm for future exploration, where missions have to operate in situ with physical interactions with the bodies or their atmospheres in poorly constrained environments, with limited a priori knowledge, and under harsh environmental conditions. We examined experiences from missions over the past two decades and highlighted how unanticipated situations arise that current systems are unable to handle without the expertise of ground operators and tools. Future autonomous systems would have to handle a wide range of conditions on their own if they were to operate in more remote destinations. Despite several demonstrations of autonomous capabilities, state of the practice remains largely reliant on ground-in-the-loop operations. The need for autonomy is driven by two main constraints: mission objectives and environmental constraints. Mission objectives are typically set to reduce the need for new technologies but environment constraints would eventually drive the need for autonomy. The adoption of autonomy at a broader scale is not only constrained by technical barriers such as the advancement of the algorithms, the integration of cross-domain capabilities, and the verification and validation of such capabilities; it is also driven by non-technical factors related to acceptable mission risk, cost, and change in the roles of humans interacting with the spacecraft. We articulate why future missions would require more autonomy: our ability to predict the execution of onboard activities seriously diminishes for in situ missions, as evidenced by two decades of Mars surface exploration. We highlight key autonomy advanced across different mission phases: in-space, proximity operations, landed, and surface missions. We conclude by sharing a scenario arguing for sending an autonomous spacecraft to a NEO to approach, land, move, and sample its surface as an adequately challenging scenario to substantially advance both the function-level and system-level autonomy elements. Such a scenario could be matured and demonstrated using SmallSats and has clear success metrics for each mission phase.

As our current missions discover a multitude of planets around other stars, we are compelled to ask ourselves what it would take some day to explore those exoplanets. A mission to perform in situ exploration of even the nearest exoplanetary system is a daunting, yet an exciting challenge. Such a mission will undoubtedly require a sophisticated level of autonomy together with major advances in power, propulsion, and other spacecraft disciplines. This is a small step toward a goal we only dare to dream about.

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This work was performed at the Jet Propulsion Laboratory, California Institute of Technology under contract to the National Aeronautics and Space Administration. Government sponsorship is also acknowledged.

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Nesnas, I.A., Fesq, L.M. & Volpe, R.A. Autonomy for Space Robots: Past, Present, and Future. Curr Robot Rep 2 , 251–263 (2021). https://doi.org/10.1007/s43154-021-00057-2

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pace Space travel is complex, expensive, and risky. Great sums and valuable payloads are on the line every time one spacecraft docks with another. One slip and a billion-dollar mission could be lost. Aerospace engineers believe that autonomous control, like the sort guiding many cars down the road today, could vastly improve mission safety, but the complexity of the mathematics required for error-free certainty is beyond anything on-board computers can currently handle.

In a new paper presented at the IEEE Aerospace Conference in March 2024, a team of aerospace engineers at Stanford University reported using AI to speed the planning of optimal and safe trajectories between two or more docking spacecraft. They call it ART – the Autonomous Rendezvous Transformer – and they say it is the first step to an era of safer and trustworthy self-guided space travel.

Hail CAESAR

In autonomous control, the number of possible outcomes is massive. With no room for error, they are essentially open-ended.

“Trajectory optimization is a very old topic. It has been around since the 1960s, but it is difficult when you try to match the performance requirements and rigid safety guarantees necessary for autonomous space travel within the parameters of traditional computational approaches,” said Marco Pavone , an associate professor of aeronautics and astronautics and co-director of the new Stanford Center for AEroSpace Autonomy Research (CAESAR) . “In space, for example, you have to deal with constraints that you typically do not have on the Earth, like, for example, pointing at the stars in order to maintain orientation. These translate to mathematical complexity.”

“For autonomy to work without fail billions of miles away in space, we have to do it in a way that on-board computers can handle,” added Simone D’Amico , an associate professor of aeronautics and astronautics and fellow co-director of CAESAR. “AI is helping us manage the complexity and delivering the accuracy needed to ensure mission safety, in a computationally efficient way.”

Researchers in the lab spaces run by the Stanford Center for AEroSpace Autonomy Research (CAESAR) | Andrew Brodhead

CAESAR is a collaboration between industry, academia, and government that brings together the expertise of Pavone’s Autonomous Systems Lab and D’Amico’s Space Rendezvous Lab . The Autonomous Systems Lab develops methodologies for the analysis, design, and control of autonomous systems – cars, aircraft, and, of course, spacecraft. The Space Rendezvous Lab performs fundamental and applied research to enable future distributed space systems whereby two or more spacecraft collaborate autonomously to accomplish objectives otherwise very difficult for a single system, including flying in formation, rendezvous and docking, swarm behaviors, constellations, and many others. CAESAR is supported by two founding sponsors from the aerospace industry and, together, the lab is planning a launch workshop for May 2024.

Our paper is exciting, I think, for including artificial intelligence components in the traditional guidance, navigation, and control pipeline to make these rendezvous smoother, faster, more fuel efficient, and safer.”

A warm start

The Autonomous Rendezvous Transformer is a trajectory optimization framework that leverages the massive benefits of AI without compromising on the safety assurances needed for reliable deployment in space. At its core, ART involves integrating AI-based methods into the traditional pipeline for trajectory optimization, using AI to rapidly generate high-quality trajectory candidates as input for conventional trajectory optimization algorithms. The researchers refer to the AI suggestions as a “warm start” to the optimization problem and show how this is crucial to obtain substantial computational speed-ups without compromising on safety.

“One of the big challenges in this field is that we have so far needed ‘ground in the loop’ approaches – you have to communicate things to the ground where supercomputers calculate the trajectories and then we upload commands back to the satellite,” explains Tommaso Guffanti , a postdoctoral fellow in D’Amico’s lab and first author of the paper introducing the Autonomous Rendezvous Transformer. “And in this context, our paper is exciting, I think, for including artificial intelligence components in traditional guidance, navigation, and control pipeline to make these rendezvous smoother, faster, more fuel efficient, and safer.”

Communications in space travel at the speed of light, about 186,000 miles per second. While this means it only takes about 1.28 seconds for a message from mission control to reach the Moon, astronauts who end up on Mars could end up waiting as much as 24 minutes.

Next frontiers

ART is not the first model to bring AI to the challenge of space flight, but in tests in a terrestrial lab setting, ART outperformed other machine learning-based architectures. Transformer models, like ART, are a subset of high-capacity neural network models that got their start with large language models, like those used by chatbots. The same AI architecture is extremely efficient in parsing, not just words, but many other types of data such as images, audio, and now, trajectories.

“Transformers can be applied to understand the current state of a spacecraft, its controls, and maneuvers that we wish to plan,” Daniele Gammelli , a postdoctoral fellow in Pavone’s lab, and also a co-author on the ART paper. “These large transformer models are extremely capable at generating high-quality sequences of data.”

The next frontier in their research is to further develop ART and then test it in the realistic experimental environment made possible by CAESAR. If ART can pass CAESAR’s high bar, the researchers can be confident that it’s ready for testing in real-world scenarios in orbit.

“These are state-of-the-art approaches that need refinement,” D’Amico says. “Our next step is to inject additional AI and machine learning elements to improve ART’s current capability and to unlock new capabilities, but it will be a long journey before we can test the Autonomous Rendezvous Transformer in space itself.”

For more information

D’Amico is also the W.M. Keck Faculty Scholar in the School of Engineering and professor of geophysics (by courtesy) in the Stanford Doerr School of Sustainability . He is the director of the AA Undergraduate Program. As member of the faculty council of the Stanford Emerging Technology Review (SETR) , D’Amico represents the Space Technology Focus Area. D’Amico and Pavone are both faculty affiliates of the Institute for Human-Centered Artificial Intelligence (HAI) . Pavone is also an associate professor (by courtesy) of electrical engineering and of computer science. He is the director of the Center for Automotive Research at Stanford , the chief faculty advisor of the Stanford Student Space Initiative , and a member of the Institute for Computational and Mathematical Engineering (ICME) .

This research was funded in part by the NASA University Leadership Initiative.

Contributions to Autonomous Operation of a Deep Space Vehicle Power System

Nasa sttr phase ii contract no. 80nssc18c0219, degree type.

• Master of Science in Electrical and Computer Engineering
• Electrical and Computer Engineering

Campus location

• West Lafayette

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, usage metrics.

• Electrical engineering not elsewhere classified

Publications

Journal Articles

Willis, M., D'Amico, S.;              Fast Angles-Only Relative Navigation Using Polynomial Dynamics ;   Advances in Space Research (2023). Available online 3 September 2023, In Press.  Kruger, J., D’Amico, S.;             Observability Analysis and Optimization for Angles-Only Navigation of Distributed Space Systems ;   Advances in Space Research (2023): Available online 3 September 2023, In Press. 

Park, T. H., D’Amico, S.;             Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap ;             Advances in Space Research (2023). Available online 24 March 2023, In Press. 

Conference Proceedings

Hunter, M., D'Amico, S.;              Robust Closed-form Framework for Drag-Propulsive Control of Formation Flight ;   IEEE Aerospace Conference, Big Sky, Montana, March 2-9, 2024. 

Lowe, S., Fitzpatrick, D., Buynovskiy, A., Shoemaker, L., Palo, S., D'Amico, S.;              Concept of Operations for SWARM-EX: a Three CubeSat Formation-Flying Mission ;   IEEE Aerospace Conference, Big Sky, Montana, March 2-9, 2024. 

Delurgio, N., D'Amico, S.;   Closed-Form Modeling and Control of Spacecraft Swarms in Eccentric Orbits ;   IEEE Aerospace Conference, Big Sky, Montana, March 2-9, 2024. 

Ahmed, Z., Park, T. H., Bhattacharjee, A., Fazel-Rezai, R., Graves, R., Saarela, O., Teramoto, R., Vemulapalli, K., D'Amico, S.;   SPEED_UE_Cube: A Machine Learning Dataset for Autonomous, Vision-Based Spacecraft Navigation ;    46th Rocky Mountain AAS Guidance, Navigation and Control Conference, Breckenridge, Colorado, February 2-7, 2024. 

Rizza, A., Topputo, F., D'Amico, S.; Goal-oriented Asteroid Mapping under Uncertainties using Sequential Convex Programming 2024 AIAA SciTech Forum and Exposition, Orlando, Florida, January 8-12, 2024. 

Kruger, J., Guffanti, T., Park, T. H., Murray-Cooper, M. Low, S. Y. W., Bell, T., D’Amico, S., Roscoe, C., Westphal, J.;              Adaptive End-to-End Architecture for Autonomous Spacecraft Navigation and Control During Rendezvous and Proximity Operations ;   2024 AIAA SciTech Forum and Exposition, Orlando, Florida, January 8-12, 2024. 

Doctoral Theses

Kruger, J.; Flight algorithms for autonomous tracking and navigation of distributed space systems using inter-satellite bearing angles Stanford University, PhD Thesis (2024).;

Published Datasets

Park, T. H., D'Amico, S.; SPE3R: Synthetic Dataset for Satellite Pose Estimation and 3D Reconstruction ; Stanford Digital Repository (2024). DOI: 10.25740/pk719hm4806.

Park, T. H., Ahmed, Z., Bhattacharjee, A., Razel-Rezai, R., Graves, R., Saarela, O., Teramoto, R., Vemulapalli, K., D'Amico, S.; Spacecraft Pose Estimation Dataset of a 3U CubeSat using Unreal Engine (SPEED-UE-Cube) ; Stanford Digital Repository (2024).

Koenig, A. W., D’Amico, S., Lightsey, G.;   Formation Flying Orbit and Control Concept for Virtual Super Optics Reconfigurable Swarm Mission ;   Journal of Guidance, Control, and Dynamics(2023).

Park, T. H., D’Amico, S.;              Adaptive Neural-Network-Based Unscented Kalman Filter for Robust Pose Tracking of Noncooperative Spacecraft ;   Journal of Guidance, Control, and Dynamics, Vol. 46, No. 9, pp. 1671-1688 (2023). DOI: 10.2514/1.G007387

Pasqualetto Cassinis, L., Park, T. H., Stacey, N., D’Amico, S., Menicucci, A., Gill, E., Ahrns, I., Sanchez-Gestido, M.; Leveraging Neural Network Uncertainty in Adaptive Unscented Kalman Filter for Spacecraft Pose Estimation ;                                 Advances in Space Research, Vol. 71, Issue 12, pp. 5061-5082 (2023). DOI: 10.1016/j.asr.2023.02.021

Park, T. H., Märtens, M., Jawaid, M., Wang, Z., Chen, B., Chin., T.-J., Izzo, D., D'Amico, S.;                                 Satellite Pose Estimation Competition 2021: Results and Analyses ;                                 Acta Astronautica, Volume 204, 2023, Pages 640-665, ISSN 0094-5765, DOI:10.1016/j.actaastro.2023.01.002.

Dennison, K., D'Amico, S.;                                 Leveraging Camera Attitude Priors for Structure from Motion of Small, Noncooperative Targets ;                                 2023 AAS/AIAA Astrodynamics Specialist Conference, Big Sky, Montana, August 13-17, 2023.

Hunter, M., D'Amico, S.;                                 Closed-form Optimal Propulsive-Differential Drag Control for Large Reconfigurations of Spacecraft Swarms ;                     2023 AAS/AIAA Astrodynamics Specialist Conference, Big Sky, Montana, August 13-17, 2023.

Dennison, K., D'Amico, S.;                                 Vision-Based 3D Reconstruction for Navigation and Characterization of Unknown, Space-Borne Targets ;                                 33rd AAS/AIAA Space Flight Mechanics Meeting, Austin, TX, January 15-19, 2023.

Lowe, S., D'Amico, S.;                                 Reduced-Order Model for Spacecraft Swarm Orbit Design ;                                 33rd AAS/AIAA Space Flight Mechanics Meeting, Austin, TX, January 15-19, 2023.

Kruger, J., D'Amico, S., Roscoe, C., Westphal, J.;                                 Angles-Only Tracking and Navigation for Approach and Rendezvous in Geosynchronous Orbits ;                                 33rd AAS/AIAA Space Flight Mechanics Meeting, Austin, TX, January 15-19, 2023.

Dennison, K.; Vision-based tracking and shape recovery of non-cooperative targets using distributed space systems Stanford University, PhD Thesis (2023).;

Policy Reports

D'Amico, S., Guffanti, T., Lee, N., Manuel, W. J., Porteous, I. D.; The Stanford Emerging Technology Review 2023: A Report on 10 Key Technologies and Their Policy Implications - Space Chapter Stanford University Public Report, published November 14, 2023

Technical Reports

Austin, A., Bapst, J., Bills, B., Berne, A., Bierson, C., Bramson, A., D’Amico, S., Denton, C. A., Ermakov, A., Evans, A., Hemingway, D., Hernandez, S., Hogstrom, K., Izquierdo, K., James, P., Johnson, B., Kahre, M., Keane, J. T., Lau, H., Navarro, T., Neveu, M., Nimmo, F., Ojha, L., Paik, H. J., Park, R., Rosen, P., Simons, M., Smith, D. E., Smrekar, S., Soderlund, K., Sori, M., Steinbrügge, G., Tikoo, S., Wagner, N., Weber, R., Vance, S., Zebker, H.;                                 Next Generation Planetary Geodesy ;                                 Keck Institute for Space Studies Report

Departmental Documents 

D'Amico, S., Pavone, M., Elschot, S., Arya, M., Sakovsky, M., Hara., K.;                                 AA Strategic Plan: Space Chapter ;                                 Stanford University, Department of Aeronautics and Astronautics, April 25, 2023

Lippe, C., D'Amico, S.;                                 Safe, Delta-v-Efficient Spacecraft Swarm Reconfiguration Using Lyapunov Stability and Artificial Potentials ;                                 Journal of Guidance, Control, and Dynamics, Vol. 45, No. 2, pp. 213-231 (2022). DOI: 10.2514/1.G006253.

Peretz, E., Hamilton, C., Mather, J., D’Amico, S., Michaels, A., Pritchett, R., Yu, W., Wizinowich, P.;                                 Astrostationary orbits for hybrid space and ground-based observatories ;                                 J. Astron. Telesc. Instrum. Syst. 8(1), 014004 (2022), DOI: 10.1117/1.JATIS.8.1.014004. 31 January, 2022.

Stacey, N., D'Amico, S.;                                  Robust Autonomous Spacecraft Navigation and Environment Characterization ;                                 American Geophysical Union, Fall Meeting, Chicago, Illinois and Online Everywhere, Virtual Poster, December 12-16 2022.

Hunter, M., D'Amico, S.;                                 Closed-form Optimal Solutions for Propulsive-Differential Drag Control of Spacecraft Swarms ;                                 2022 AAS/AIAA Astrodynamics Specialist Conference, Charlotte, North Carolina, August 7-11 2022.

Manuel, W. J., D'Amico, S.;                                 Optimal Control in the Circular Restricted Three-Body Problem Using Integration Constants ;                                 2022 AAS/AIAA Astrodynamics Specialist Conference, Charlotte, North Carolina, August 7-11 2022.

Park, T. H., D'Amico, S.;                                 Adaptive Neural Network-based Unscented Kalman Filter for Spacecraft Pose Tracking at Rendezvous ;                                 2022 AAS/AIAA Astrodynamics Specialist Conference, Charlotte, North Carolina, August 7-11 2022.

Guffanti, T., D'Amico, S.;                                 Robust Passively Safe Spacecraft Swarming via Closed-form and Optimization-based Control Approaches ;                                 American Control Conference, Atlanta, Georgia, June 8-10 (2022).

Park, T. H., D’Amico, S.;                                 Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap;                                  11th International Workshop on Satellite Constellations & Formation Flying, Milano, Italy, June 7-10 (2022).

Kruger, J., D'Amico, S.;                                 Observability Analysis and Optimization for Angles-Only Navigation of Distributed Space Systems ;                                 11th International Workshop on Satellite Constellations & Formation Flying, Milano, Italy, June 7-10 (2022).

Stacey, N., Dennison, K., D'Amico, S.;                                 Autonomous Asteroid Characterization through Nanosatellite Swarming ;                                 IEEE Aerospace Conference, Big Sky, Montana, March 5-12 (2022).

Lowe, S., Markevitch, M., D'Amico, S.;                                 Relative Navigation and Pointing Error Budget for an X-ray Astronomy Formation-Flying Mission ;                                 44th Annual AAS Guidance, Navigation, and Control Conference, Breckenridge, Colorado, February 4-9, 2022.

Iiyama, K., Kruger, J., D'Amico, S.;                                 Autonomous Distributed Angles-Only Navigation and Timekeeping in Lunar Orbit ;                                 ION International Technical Meeting, Long Beach, California, January 24-27 (2022).

Sori, M., Ermakov, A., Keane, J., Bierson, C., Bills, B., Bramson, A., D'Amico, S., Evans, A., Hemingway, D., Izquierdo, K., James, P., Johnson, B., Kahre, M., Navarro, T., O'Rourke, J., Ohja, L., Paik, H., Park, R., Simons, M., Smith, D., Smrekar, S., Soderlund, K., Steinbrügge, G., Tikoo, S., Vance, S., Wagner, N., Weber, R., Zebker, H.;                                 Compelling Science Enabled by Gravity Investigations at Mars ;                                 Low-Cost Science Mission Concepts for Mars Exploration Workshop, Southern California, January 11-13 (2022).

Willis, M.;                                 Analytical theory of satellite relative motion with applications to autonomous navigation and control                                  Stanford University, PhD Thesis (2022).

Guffanti, T.;                                 Optimal Passively-Safe Control of Multi-Agent Motion with Application to Distributed Space Systems                                  Stanford University, PhD Thesis (2022).

Public Study Reports

Bruce Macintosh, Simone D’Amico, Adam Koenig, Eduardo Bendek, Keith Grogran, Stuart Shaklan, A. Madurowicz, R. de Rosa, T. Greene, J. Debes, E. Douglas, R. Jensen-Clem, G. Duchene, T. Esposito.;                                 Miniature Distributed Occulter Telescope (mDOT) Project Report ;                                 Publicly released project report (Uploaded June 1, 2022).

Kruger, J., D'Amico, S.;                                 Autonomous Angles-Only Multitarget Tracking for Spacecraft Swarms ;                                 Acta Astronautica, Volume 189, December 2021, pp. 514-529. DOI: https://doi.org/10.1016/j.actaastro.2021.08.049

Romero-Wolf, A., Bryden, G., Agnes, G., Arenberg, J. W., Bradford, S. C., D’Amico, S., Debes, J., Greenhouse, M., Hu, R., Matousek, S., Rhodes, J., Ziemer, J.;                                 Starshade Rendezvous: exoplanet orbit constraints from multi-epoch direct imaging ;                                 J. Astron. Telesc. Instrum. Syst. 7(2) 021219 (June, 2021). DOI: https://doi.org/10.1117/1.JATIS.7.2.021219

Sullivan, J., Koenig, A. W., Kruger, J., D'Amico, S.;                                 Generalized Angles-Only Navigation Architecture for Autonomous Distributed Space Systems ;                                 Journal of Guidance, Control, and Dynamics, Vol. 44, No. 6 (April, 2021), pp. 1087-1105. DOI: doi/abs/10.2514/1.G005439

Lippe, C., D'Amico, S.;                                 Chief Spacecraft Orbit Refinement for Optimal Spacecraft Swarm Reconfiguration ;                                 Acta Astronautica, Volume 183, June 2021, pp. 162-175. DOI: https://doi.org/10.1016/j.actaastro.2021.03.011

Koenig, A. W., D'Amico, S.;                                 Fast Algorithm for Fuel-Optimal Impulsive Control of Linear Systems with Time-Varying Cost ;                                 IEEE Transactions on Automatic Control (2021). DOI: 10.1109/TAC.2020.3027804

Chernick, M., D'Amico, S.;                                 Closed-Form Optimal Impulsive Control of Spacecraft Formations Using Reachable Set Theory ;                                 Journal of Guidance, Control, and Dynamics, Vol. 44, No. 1, pp. 25-44 (2021). DOI: doi/abs/10.2514/1.G005218

Peretz E., Mather J., Hall K., Pabarcius L., Canzoniero C., Gilchrist K., Lierber-Kotz M., Slonaker R.,Yu W., Hughes S., Hur-Diaz S., Koenig A., D'Amico S.;                                 Exoplanet imaging scheduling optimization for an orbiting starshade working with Extremely Large Telescopes ;                                 Journal of Astronomical Telescopes, Instruments, and Systems 7(2), 021213 (2021). DOI: 10.1117/1.JATIS.7.2.021213

Agarwal, R., Oh, B., Fitzpatrick, D., Buynovskiy, A., Lowe, S., Lisy, C., Kriezis, A., Lan, B., Lee, Z., Thomas, A., Wallace, B., Costantino, E., Miner, G., Thayer, J., D’Amico, S., Lemmer, K., Lohmeyer, W., Palo, S.;                                 Coordinating Development of the SWARM-EX CubeSat Swarm Across Multiple Institutions ;                                 35th Annual Conference on Small Satellites, Utah State University, August 7-12 (2021).

Park, T. H., Bosse, J., D’Amico, S.;                                  Robotic Testbed for Rendezvous and Optical Navigation: Multi-Source Calibration and Machine Learning Use Cases ;                                 2021 AAS/AIAA Astrodynamics Specialist Conference, Big Sky, Virtual, August 9-11 (2021).

Willis, M., D'Amico, S.;                                 Cartesian Relative Motion on Perturbed Eccentric Orbits and Closed-Form Solution for J2 Effects at Low Inclinations ;                                 2021 AAS/AIAA Astrodynamics Specialist Conference, Big Sky, Virtual, August 9-11 (2021).

Koenig A. W., Kruger J., Sullivan J., D'Amico S.;                                 ARTMS: Enabling Autonomous Distributed Angles-Only Orbit Estimation for Spacecraft Swarms ;                                 2021 American Control Conference, New Orleans, Louisiana, May 26-28 (2021).

Koenig A. W., D'Amico S., Peretz E., Yu W., Hur-Diaz S., Mather J.;                                 Optimal Spacecraft Orbit Design for Inertial Alignment with Ground Telescopes ;                                 IEEE Aerospace Conference, Yellowstone Conference Center, Big Sky, Montana, March 6-13 (2021).

Lippe C., D'Amico S.;                                 Minimization of Delta-v for Satellite Swarm Maintenance in Eccentric Orbits using a Virtual Chief ;                                 IEEE Aerospace Conference, Yellowstone Conference Center, Big Sky, Montana, March 6-13 (2021).

Dennison K., D'Amico S.;                                 Comparing Optical Tracking Techniques in Distributed Asteroid Orbiter Missions Using Ray-Tracing ;                                 AAS/AIAA Space Flight Mechanics Meeting, Virtual Event, February 1-4 (2021).

Willis M., D'Amico S.;                                 Analytical Description of Relative Position and Velocity on J2-Perturbed Eccentric Orbits ;                                 AAS/AIAA Space Flight Mechanics Meeting, Virtual Event, February 1-4 (2021).

Stacey N., D'Amico S.;                                 Process Noise Covariance Modeling For Absolute and Relative Orbit Determination ;                                 AAS/AIAA Space Flight Mechanics Meeting, Virtual Event, February 1-4 (2021).

Giralo V., D'Amico S.;                                 Precise Real-Time Relative Orbit Determination for Large Baseline Formations Using GNSS ;                                 Institute of Navigation, International Technical Meeting, Virtual Event, January 25-28 (2021).

Guffanti T., D'Amico S.;                                 Multi-Agent Passive Safe Optimal Control using Integration Constants as State Variables ;                                 AIAA Scitech 2021 Forum, Virtual Event, January 11-15 & 19-21 (2021). DOI: 10.2514/6.2021-1101

Lippe C., D'Amico S.;                                 Autonomous Reference Orbit Refinement for Optimal Swarm Reconfiguration ;                                 AIAA Scitech 2021 Forum, Virtual Event, January 11-15 & 19-21 (2021).

Lippe C.;                                 Optimal Guidance and Control of Spacecraft Swarms in Planetary and Asteroid Orbits                                  Stanford University, PhD Thesis (2021).

Giralo V.;                                 Precision Navigation of Miniaturized Distributed Space Systems using GNSS ;                                 Stanford University, PhD Thesis (2021).

Chernick M.;                                 Optimal Impulsive Control of Spacecraft Relative Motion ;                                 Stanford University, PhD Thesis (2021).

Park, T. H., Märtens, M., Lecuyer, G., Izzo, D., D’Amico, S.;                                 Next Generation Spacecraft Pose Estimation Dataset (SPEED+) ;                                 Stanford Digital Repository (2021). DOI: https://doi.org/10.25740/wv398fc4383 .

Lippe C., D'Amico S.;                                 Spacecraft Swarm Dynamics and Control About Asteroids ;                                 Advances in Space Research (December, 2020), DOI: 10.1016/J.ASR.2020.06.037.

Lippe C., D'Amico S.;                                 Minimization of Delta-v for Satellite Swarm Maintenance Using a Virtual Chief ;                                 IEEE Journal on Miniaturization for Air and Space Systems (2020). DOI: 10.1109/JMASS.2020.3026633.

Kisantal M., Sharma S., Park T. H., Izzo D., Märtens M., D'Amico S.;                                 Satellite Pose Estimation Challenge: Dataset, Competition Design and Results ;                                 IEEE Transactions on Aerospace and Electronic Systems, Vol. 56, No. 5, pp. 4083-4098 (2020). DOI: 10.1109/TAES.2020.2989063

Sharma S., D'Amico S.;                                 Neural Network-Based Pose Estimation for Noncooperative Spacecraft Rendezvous ;                                 IEEE Transactions on Aerospace and Electronic Systems (2020). DOI: 10.1109/TAES.2020.2999148.

Palo S.E., Pilinski M., Thayer J.P., Lohmeyer W.Q., Lemmer K.M., D'Amico S., Lightsey G.E., Latif S.;                                 The Space Weather Atmospheric Reconfigurable Multiscale Experiment (SWARM-EX): A New NSF Supported CubeSat Project ;                                 American Geophysical Union, Fall Meeting, Online Everywhere, December 1-17 (2020).

Giralo V., Chernick M., D'Amico S.;                                 Guidance, Navigation, and Control for the DWARF Formation-Flying Mission ;                                 2020 AAS/AIAA Astrodynamics Specialist Conference, South Lake Tahoe, California, August 9 - 13 (2020).

Koenig A. W., D'Amico S.;                                 Observability-Aware Numerical Algorithm for Angles-Only Initial Relative Orbit Determination ;                                 2020 AAS/AIAA Astrodynamics Specialist Conference, South Lake Tahoe, California, August 9 - 13 (2020).

Kruger J., D'Amico S.;                                 Autonomous Angles-Only Multi-Target Tracking for Spacecraft Swarms ;                                 2020 AAS/AIAA Astrodynamics Specialist Conference, South Lake Tahoe, California, August 9 - 13 (2020).

Lippe C., D'Amico S.;                                 Adaptive Filter for Osculating-to-Mean Relative Orbital Elements (ROE) Conversion ;                                 2020 AAS/AIAA Astrodynamics Specialist Conference, South Lake Tahoe, California, August 9 - 13 (2020).

Park T. H., D'Amico S.;                                 Generative Model for Spacecraft Image Synthesis using Limited Dataset ;                                 2020 AAS/AIAA Astrodynamics Specialist Conference, South Lake Tahoe, California, August 9 - 13 (2020).

Willis M., D'Amico S.;                                  Applications and Limitations of Angles-Only Relative Navigation Using Polynomial Solutions ;                                 2020 AAS/AIAA Astrodynamics Specialist Conference, South Lake Tahoe, California, August 9 - 13 (2020).

Sullivan J.;                                 Nonlinear Angles-Only Orbit Estimation for Autonomous Distributed Space Systems ;                                 Stanford University, PhD Thesis (2020).

Sharma S., Park T. H., D'Amico S.;                                 Spacecraft Pose Estimation Dataset (SPEED) ;                                 Stanford Digital Repository (2020).

Giralo V., D'Amico S.;                                 Distributed Multi-GNSS Timing and Localization for Nanosatellites ;                                 Navigation: Journal of The Institute of Navigation, Vol. 66, No. 4, pp. 729-746 (2019). DOI: 10.1002/navi.337

Koenig A. W., Macintosh B., D'Amico S.;                                 Formation Design of Distributed Telescopes in Earth Orbit for Astrophysics Applications ;                                 Journal of Spacecraft and Rockets (2019). DOI: 10.2514/1.A34420

Guffanti T., D'Amico S.;                                 Linear Models for Spacecraft Relative Motion Perturbed by Solar Radiation Pressure ;                                 Journal of Guidance, Control, and Dynamics, Vol. 42, No. 9, pp. 1962-1981 (2019). DOI: 10.2514/1.G002822.

Beierle C., D'Amico S.;                                 Variable Magnification Optical Stimulator for Training and Validation of Spaceborne Vision-Based Navigation ;                                 Journal of Spacecraft and Rockets (2019). DOI: 10.2514/1.A34337

Di Mauro G., Spiller D., Bevilacqua R., D’Amico S.;                                 Spacecraft Formation Flying Reconfiguration with Extended and Impulsive Maneuvers ;                                 Journal of the Franklin Institute, Volume 356, Issue 6, pp. 3474-3507 (April 2019). https://doi.org/10.1016/j.jfranklin.2019.02.012

Macintosh, B., D'Amico, S., Koenig, A., Madurowicz, A.;                                 The Space Weather Atmospheric Reconfigurable Multiscale Experiment (SWARM-EX): A New NSF Supported CubeSat Project ;                                 American Geophysical Union, Fall Meeting, December 1-17 (2019).

Seager, S., Kasdin, N. J., Booth, J., Greenhouse, M., Lisman, D., Macintosh, B., Shaklan, S., Vess, M., Warwick, S., Webb, D., D'Amico, S., Debes, J., Domagal-Goldman, S., Hildebrandt, S., Hu, R., Hughes, M., Kiessling, A., Lewis, N., Rhodes, J., Rizzo, M. R. A., Robinson, T.; Rogers, L., Savransky, D., Scharf, D., Stark, C., Turnbull, M., Romero-Wolf, A., Ziemer, J., Gray, A., Hughes, M., Agnes, G., Arenberg, J., Bradford, S.; Fong, M., Gregory, J., Matousek, S., Murphy, J., Willems, P.;                                 Starshade Rendezvous Probe Mission ;                                 Astro2020: Decadal Survey on Astronomy and Astrophysics, APC white papers, no. 106; Bulletin of the American Astronomical Society, Vol. 51, Issue 7, id. 106 (2019).

Arenberg, J., O'Meara, J., Tumlinson, J., Gelino, D., Garcia, M., Carey, S., Wolk, S., Wolk, N., D'Amico, S, Wirz, R., Genet, R., Barnhart, D., Freed, R., Tock, K.;                                 Stella Splendida: Building the science and engineering workforce of the 21st Century ;                                 Astro2020: Decadal Survey on Astronomy and Astrophysics, APC white papers, no. 68; Bulletin of the American Astronomical Society, Vol. 51, Issue 7, id. 68 (2019).

Mather, J., Arenberg, J., D'Amico, S., Cash, W., Greenhouse, M., Harness, A., Hoerbelt, T., Kain, I., Kausch, W., Kimeswenger, S., Lisse, C., Martin, S., Noll, S., Peretz, E., Przybilla, N., Seager, S., Shaklan, S., Snellen, I., Willems, P.;                                 Orbiting Starshade: Observing Exoplanets at visible wavelengths with GMT, TMT, and ELT ;                                 Astro2020: Decadal Survey on Astronomy and Astrophysics, APC white papers, no. 48; Bulletin of the American Astronomical Society, Vol. 51, Issue 7, id. 48 (2019).

Willis M., Alfriend T., D'Amico S.;                                 Second-Order Solution for Relative Motion on Eccentric Orbits in Curvilinear Coordinates ;                                 2019 AAS/AIAA Astrodynamics Specialist Conference, Portland, Maine, August 11 - 15 (2019).

Stacey N., D'Amico S.;                                 Adaptive and Dynamically Constrained Process Noise Estimation for Orbit Determination ;                                 2019 AAS/AIAA Astrodynamics Specialist Conference, Portland, Maine, August 11 - 15 (2019).

Catanoso D., Kempf F., Schilling K., D'Amico S.;                                 Networked Model Predictive Control for Satellite Formation-Flying ;                                 2019 International Workshop on Satellite Constellations and Formation Flying (IWSCFF), University of Strathclyde Glasglow, UK, July 16 – 19 (2019).

D'Amico S., Koenig A., Macintosh B., Mauro D.;                                 System Design of the Miniaturized Distributed Occulter/Telescope (mDOT) Science Mission ;                                 33rd Annual Conference on Small Satellites, Utah State University, August 3-8 (2019).

Willis M., Lovell A., D'Amico S.;                                 Second Order Analytical Solution for Relative Motion on Arbitrarily Eccentric Orbits ;                                 29th AAS/AIAA Space Flight Mechanics Meeting, Ka'anapali, Maui, HI, January 13-17 (2019).

Sharma S., D'Amico S.;                                 Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Neural Networks ;                                 29th AAS/AIAA Space Flight Mechanics Meeting, Ka'anapali, Maui, HI, January 13-17 (2019).

Technical Notes

D'Amico S., Giralo, V.;                                 Evaluation of GPS Altitude Accuracy of the LX9000 High Altitude Flight Recorder ;                                 Technical Note, Stanford Space Rendezvous Lab (SLAB), September 17 (2019).

Park T. H., D'Amico S.;                                 ESA Pose Estimation Challenge 2019 ;                                 Technical Note, Stanford Space Rendezvous Lab (SLAB), July 3 (2019).

Koenig A., D'Amico S.;                                 Orbit Design and Control for the Earth-Orbiting Starshade Mission ;                                 Technical Note, Stanford Space Rendezvous Lab (SLAB), September 30 (2019).

Sharma S.;                                 Pose Estimation of Uncooperative Spacecraft using Monocular Vision and Deep Learning ;                                 Stanford University, PhD Thesis (2019).

Beierle C.;                                 High Fidelity Validation of Vision-Based Sensors and Algorithms for Spaceborne Navigation ;                                 Stanford University, PhD Thesis (2019).

Fact Sheets

D'Amico S.;                                  Miniaturized Distributed Occulter/Telescope (mDOT) ;                                 Mission Fact Sheet, Stanford Space Rendezvous Lab (SLAB), July 16 (2019).

D'Amico S.;                                 Autonomous Nanosatellite Swarming using Radio-Frequency and Optical Navigation (ANS) ;                                 NASA Fact Sheet, Stanford Space Rendezvous Lab (SLAB), July 16 (2019).

D'Amico S.;                                 Distributed multi-GNSS Timing and Localization System (DiGiTaL) ;                                 NASA Fact Sheet, Stanford Space Rendezvous Lab (SLAB), July 16 (2019).

Koenig A. W., D'Amico S.;                                 Safe Spacecraft Swarm Deployment and Acquisition in Perturbed Near-Circular Orbits Subject to Operational Constraints ;                                 Acta Astronautica, Vol. 153, pp. 297-310 (December 2018). https://doi.org/10.1016/j.actaastro.2018.01.037 .

Koenig A. W., D’Amico S.;                                 Robust and Safe N-Spacecraft Swarming in Perturbed Near-Circular Orbits ;                                 Journal of Guidance, Control, and Dynamics, Vol. 41, No. 8, pp. 1643-1662 (2018). https://doi.org/10.2514/1.G003249 .

Willis M., D’Amico S.;                                 Analytical Approach to Formation-Flying with Low-Thrust Relative Spiral Trajectories ;                                 Acta Astronautica, Vol. 153, pp. 175-190 (2018). https://doi.org/10.1016/j.actaastro.2018.02.002 .

Di Mauro G., Bevilacqua R.,Spiller D., Sullivan J., D’Amico S.;                                 Continuous Maneuvers for Spacecraft Formation-Flying Reconfiguration using Relative Orbit Elements ;                                 Acta Astronautica, Vol. 153, pp. 311-326 (2018). https://doi.org/10.1016/j.actaastro.2018.01.043 .

Chernick M., D'Amico S.;                                 New Closed-Form Solutions for Optimal Impulsive Control of Spacecraft Relative Motion ;                                 Journal of Guidance, Control, and Dynamics, Vol. 41, No. 2, pp. 301-319 (2018).

Sharma S., Ventura, J., D’Amico S.;                                 Robust Model-Based Monocular Pose Initialization for Noncooperative Spacecraft Rendezvous ;                                 Journal of Spacecraft and Rockets (2018).

Stacey N., D'Amico S.;                                 Autonomous Swarming for Simultaneous Navigation and Asteroid Characterization ;                                 2018 AAS/AIAA Astrodynamics Specialist Conference, Snowbird, UT, August 19-23 (2018).

Sullivan J., Lovell A., D'Amico S.;                                 Angles-Only Navigation for Autonomous On-Orbit Space Situational Awareness Applications ;                                 2018 AAS/AIAA Astrodynamics Specialist Conference, Snowbird, UT, August 19-23 (2018).

Chernick M., D'Amico S.;                                 Closed-Form Optimal Impulsive Control of Spacecraft Formations using Reachable Set Theory ;                                 2018 AAS/AIAA Astrodynamics Specialist Conference, Snowbird, UT, August 19-23 (2018).

Sanchez H., McIntosh D., Cannon H., Pires C., Sullivan J., O'Connor B., D'Amico S.;                                 Starling1: Swarm Technology Demonstration ;                                 32nd Annual Small Satellite Conference, AIAA/USU, SSC18-VII-08, Logan, UT, August 4-9 (2018).

Beierle C., Norton A., Macintosh B., D’Amico S.;                                 Two-Stage Attitude Control for Direct Imaging of Exoplanets with a CubeSat Telescope ;                                 SPIE 2018 Astronomical Telescopes + Instrumentation, Austin, Texas, June 10-15 (2018).

Guffanti T., D’Amico S.;                                 Integration Constants as State Variables for Optimal Path Planning ;                                 European Control Conference, Lymassol, Cyprus, June 12-15 (2018).

Giralo V., D’Amico S.;                                 Development of the Stanford GNSS Navigation Testbed for Distributed Space Systems ;                                 Institute of Navigation, International Technical Meeting, Reston, Virginia, January 29-February 1 (2018).

Sharma S., Beierle C., D’Amico S.;                                 Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks ;                                 IEEE Aerospace Conference, Yellowstone Conference Center, Big Sky, Montana, March 3-10 (2018).

Taylor M., et al.;                                 Polar Orbiting Infrared Tracking Receiver (POINTR) ;                                 IEEE Aerospace Conference, Yellowstone Conference Center, Big Sky, Montana, March 3-10 (2018).

Sharma S., Beierle C., D'Amico S.;                                 Generative Adversarial Networks for High-Fidelity Simulation of Spacecraft Proximity Operations ;                                 Technical Note, Stanford Space Rendezvous Lab (SLAB), April 23 (2018).

 Sullivan J., D’Amico S.;                                 Nonlinear Kalman Filtering for Improved Angles-Only Navigation Using Relative Orbital Elements ;                                 Journal of Guidance, Control, and Dynamics, Vol. 40, No. 9, pp. 2183-2200 (September 2017).

Sullivan J., Grimberg S., D’Amico S.;                                 Comprehensive Survey and Assessment of Spacecraft Relative Motion Dynamics Models ;                                 Journal of Guidance, Control, and Dynamics, Vol. 40, No. 8, pp. 1837-1859 (August 2017).

Sharma S., Beierle C., D’Amico S.;                                 Towards Pose Determination for Non-Cooperative Spacecraft Rendezvous using Convolutional Neural Networks ;                                 International Conference on Space Situational Awareness (ICSSA), Orlando, Florida, November 13-15 (2017).

Koenig A.W., Guffanti T., D'Amico S.;                                  New State Transition Matrices for Spacecraft Relative Motion in Perturbed Orbits ;                                 Journal of Guidance, Control, and Dynamics, Vol. 40, No. 7, pp. 1749-1768 (September 2017).

Di Mauro G., Bevilacqua R.,Spiller D., Sullivan J., D’Amico S.;                                 Continuous Maneuvers for Spacecraft Formation-Flying Reconfiguration using Relative Orbit Elements ;                                 9th International Workshop on Satellite Constellations and Formation Flight (IWSCFF), The University of Colorado Boulder, Colorado, June 19-21, 2017.

Willis M., D’Amico S.;                                  Analytical Approach to Formation Flying with Low-Thrust Relative Spiral Trajectories ;                                 9th International Workshop on Satellite Constellations and Formation Flight (IWSCFF), The University of Colorado Boulder, Colorado, June 19-21, 2017.

Koenig A., D’Amico S.;                                  Safe Spacecraft Swarm Deployment and Acquisition in Perturbed Near-Circular Orbits subject to Operational Constraints ;                                 9th International Workshop on Satellite Constellations and Formation Flight (IWSCFF), The University of Colorado Boulder, Colorado, June 19-21, 2017.

Beierle C., Sullivan J., D’Amico S.;                                 Design and Utilization of the Stanford Vision-Based Navigation Testbed for Spacecraft Rendezvous ;                                 9th International Workshop on Satellite Constellations and Formation Flight (IWSCFF), The University of Colorado Boulder, Colorado, June 19-21, 2017.

Willis M., D’Amico S.;                                  Relative Spiral Trajectories for Low-Thrust Formation Flying ;                                 26th International Symposium on Space Flight Dynamics (ISSFD), Matsuyama, Japan, June 3-9, 2017.

Koenig A., D’Amico S.;                                 Robust and Safe N-Spacecraft Swarming in Perturbed Near-Circular Orbits ;                                 26th International Symposium on Space Flight Dynamics (ISSFD), Matsuyama, Japan, June 3-9, 2017.

Beierle C., Sullivan J., D’Amico S.;                                  High-Fidelity Verification of Vision-Based Sensors for Inertial and Far-Range Spacecraft Navigation ;                                 26th International Symposium on Space Flight Dynamics (ISSFD), Matsuyama, Japan, June 3-9, 2017.

Sullivan J., D’Amico S.;                                 Adaptive Filtering for Maneuver-Free Angles-Only Navigation in Eccentric Orbits ;                                 27th AAS/AIAA Space Flight Mechanics Meeting, San Antonio, Texas , February 5-9, 2017.

Guffanti T., D’Amico S., Lavagna M.;                                 Long-Term Analytical Propagation of Satellite Relative Motion in Perturbed Orbits ;                                 27th AAS/AIAA Space Flight Mechanics Meeting, San Antonio, Texas , February 5-9, 2017.

Steindorf L., D’Amico S., Scharnagl J., Kempf F., Schilling K.;                                 Constrained Low-Thrust Satellite Formation-Flying using Relative Orbit Elements ;                                 27th AAS/AIAA Space Flight Mechanics Meeting, San Antonio, Texas , February 5-9, 2017.

Sharma S., D’Amico S.;                                  Reduced-Dynamics Pose Estimation for Non-Cooperative Spacecraft Rendezvous using Monocular Vision ;                                 40th Annual AAS Guidance and Control Conference, Breckenridge, Colorado, February 2-8, 2017.

Giralo V., Eddy D., D'Amico S.;                                 Development and Verication of the Stanford GNSS Navigation Testbed for Spacecraft Formation Flying ;                                 Technical Note, Stanford Space Rendezvous Lab (SLAB), July 18, 2017.

Sharma S., D’Amico S.;                                 Comparative Assessment of Techniques for Initial Pose Estimation using Monocular Vision ;                                 Acta Astronautica, 123 pp. 435-445 (2016).                                 DOI: 10.1016/j.actaastro.2015.12.032

Sharma S., Koenig A., Sullivan J., D'Amico S.;                                 Verification of Light-box Devices for Earth Albedo Simulation ;                                 Technical Note, Stanford Space Rendezvous Lab (SLAB), January (2016).

Koenig A.W., Guffanti T., D'Amico S.;                                 New State Transition Matrices for Relative Motion of Spacecraft Formations in Perturbed Orbits ;                                 AIAA Space and Astronautics Form and Exposition, SPACE 2016, Long Beach Convention Center California, 13-16 September, 2016.

Chernick M., D'Amico S.;                                 New Closed-Form Solutions for Optimal Impulsive Control of Spacecraft Relative Motion ;                                 AIAA Space and Astronautics Form and Exposition, SPACE 2016, Long Beach Convention Center California, 13-16 September, 2016.

Riggi L., D'Amico S.;                                 Optimal Impulsive Closed-Form Control for Spacecraft Formation Flying and Rendezvous ;                                 2016 American Control Conference, July 6–8, Boston, MA, USA (2016).

Kolmas J., Banazadeh P., Koenig A.W., D‘Amico S.,Macintosh B.;                                 System Design of a Miniaturized Distributed Occulter/Telescope for Direct Imaging of Star Vicinity ;                                 IEEE Aerospace Conference, Yellowstone Conference Center, Big Sky, Montana, March 5-12, 2016.

Sullivan J., Koenig A., D‘Amico S.;                                 Improved Maneuver-Free Approach to Angles-Only Navigation for Space Rendezvous ;                                 26th AAS/AIAA Space Flight Mechanics Meeting, Napa, CA, February 14-18, 2016.

Allende G., D'Amico S., Montenbruck O., Hugentobler U., Delpech M.;                                 Radio-Frequency Sensor Fusion for Relative Navigation of Formation Flying Satellites ;                                 International Journal of Space Science and Engineering, Vol. 3, No. 2, pp. 129-147 (2015).                                 DOI: 10.1504/IJSPACESE.2015.072333

Wermuth M., Gaias G., D’Amico S.;                                 Safe Picosatellite Release from a Small Satellite Carrier ;                                 Journal of Spacecraft and Rockets, Vol. 52, N.5, pp. 1338-1347 (2015).                                 DOI: 10.2514/1.A33036

Gaias G., D’Amico S.;                                 Impulsive Maneuvers for Formation Reconfiguration using Relative Orbital Elements ;                                 Journal of Guidance, Control, and Dynamics, Vol. 38, No. 6, pp. 1036-1049 (2015).                                 DOI: 10.2514/1.G000189

Gaias G., D'Amico S., Ardaens J.-S.;                                 Generalized Multi-Impulsive Maneuvers for Optimum Spacecraft Rendezvous in Near-Circular Orbit ;                                 International Journal of Space Science and Engineering, Vol.3, No.1, pp.68 - 88 (2015)                                 DOI: 10.1504/IJSPACESE.2015.069361

Koenig A.W., D'Amico S., Macintosh B., Titus C.J.;                                 Formation Design Analysis of a Miniaturized Distributed Occulter/Telescope in Earth Orbit ;                                 25th International Symposium on Space Flight Dynamics ISSFD, Munich, Germany, October 19 - 23, 2015.

Singh S., D'Amico S., Pavone M.;                                 High-Fidelity Modeling and Control System Synthesis for a Drag-Free Microsatellite ;                                 25th International Symposium on Space Flight Dynamics ISSFD, Munich, Germany, October 19 - 23, 2015.

Koenig A.W., D'Amico S., Macintosh B., Titus C.J.;                                 A Pareto-Optimal Characterization of Miniaturized Distributed Occulter/Telescope Systems ;                                 SPIE Optics + Photonics 2015, San Diego, California, United States, 9 - 13 August 2015.

Koenig A.W., D'Amico S., Macintosh B., Titus C.J.;                                 Optimal Formation Design of a Miniaturized Distributed Occulter/Telescope in Earth Orbit ;                                 2015 AAS/AIAA Astrodynamics Specialist Conference, Vail, Colorado, United States, 9 - 13 August 2015.

Sharma S., D’Amico S.;                                 Comparative Assessment of Techniques for Initial Pose Estimation using Monocular Vision ;                                 8th International Workshop on Satellite Constellations and Formation Flying, IWSCFF 2015, 8-10 June, Delft University of Technology (2015).

D’Amico S., Pavone M., Saraf S., Alhussien A., Al-Saud T., Buchman S., Byer R., Farhat C.;                                 Miniaturized Autonomous Distributed Space System for Future Science and Exploration ;                                 8th International Workshop on Satellite Constellations and Formation Flying, IWSCFF 2015, 8-10 June, Delft University of Technology (2015).

D’Amico S., Benn M., and Jørgensen J.L.;                                 Pose Estimation of an Uncooperative Spacecraft from Actual Space Imagery ;                                 International Journal of Space Science and Engineering, Vol.2, No.2, pp.171 - 189 (2014).                                 DOI: 10.1504/IJSPACESE.2014.060600

Gaias G., D’Amico S., and Ardaens J.-S.;                                 Angles-Only Navigation to a Noncooperative Satellite Using Relative Orbital Elements ;                                 Journal of Guidance, Control, and Dynamics, 37(2): 439-451,2014                                 DOI: 10.2514/1.61494

De Florio S., D'Amico S., Radice G.;                                 Virtual Formation Method for Precise Autonomous Absolute Orbit Control ;                                 Journal of Guidance, Control, and Dynamics, 37(2): 425-438, 2014                                 DOI: 10.2514/1.61575

Gaias G., Ardaens J.-S., and D’Amico S.;                                 The Autonomous Vision Approach Navigation and Target Identification (AVANTI) Experiment: Objectives and Design ;                                 ESA GNC 2014, 9th International ESA Conference on Guidance, Navigation & Control Systems, 2-6 June 2014, Oporto, Portugal (2014).

Ardaens J.-S., D’Amico S., and Sommer J.;                                 GPS Navigation System for Challenging Close-Proximity Formation-Flight ;                                 24th International Symposium on Spaceflight Dynamics, 5-9 May 2014, Laurel, USA (2014).

Kahle R., D’Amico S.;                                 The TerraSAR-X Precise Orbit Control – Concept and Flight Results ;                                 24th International Symposium on Space Flight Dynamics, 5-9 May 2014, Laurel, USA (2014).

Wermuth M., D’Amico S., and Gaias G.;                                 Safe Release of a Picosatellite from a Small Satellite Carrier in Low Earth Orbit ;                                 24th AAS/AIAA Space Flight Mechanics Meeting, 26-30 Jan. 2014, Santa Fe, USA (2014).

Ardaens J.-S., D'Amico S., Cropp A.;                                 GPS-based Relative Navigation for the Proba-3 Formation Flying Mission;                                 Acta Astronautica, 91: 341-355, 2013                                 DOI 10.1016/j.actaastro.2013.06.025

De Florio S., D'Amico S., Radice G.;                                 Flight Results of Precise Autonomous Orbit Keeping Experiment on PRISMA Mission;                                 Journal of Spacecraft and Rockets, 50 (3): 662-674, 2013                                 DOI 10.2514/1.A32347

D'Amico S., Ardaens J.-S., De Florio, S.;                                 Autonomous Formation Flying based on GPS - PRISMA Flight Results;                                 Acta Astronautica, 82 (1): 69-79, 2013                                 DOI 10.1016/j.actaastro.2012.04.033

D'Amico S., Ardaens J., Gaias G., Benninghoff H., Schlepp B., Joergensen J. L.;                                 Noncooperative Rendezvous using Angles-only Optical Navigation: System Design and Flight Results;                                 Journal of Guidance, Control, and Dynamics, 36 (6): 1576-1595, 2013                                 DOI 10.2514/1.59236

Allende G., D'Amico S., Montenbruck O., Hugentobler U., Delpech M.;                                 FFRF and GPS Sensor Fusion for Relative Navigation of Formation Flying Satellites;                                 5th International Conference on Spacecraft Formation Flying Missions and Technologies, 29-31 May, 2013, Munich, Germany (2013);

Gaias G., D'Amico S., Ardaens J.-S.,                                 Generalized Multi-Impulsive Maneuvers for Optimum Spacecraft Rendezvous;                                 5th International Conference on Spacecraft Formation Flying Missions and Technologies, 29-31 May, 2013, Munich, Germany (2013);

D'Amico S., Benn M., Joergensen J. L.;                                 Pose Estimation of an Uncooperative Spacecraft from Actual Space Imagery;                                 5th International Conference on Spacecraft Formation Flying Missions and Technologies, 29-31 May, 2013, Munich, Germany (2013);

De Florio S., D'Amico S., Radice G.;                                 Combined Autonomous Absolute and Relative Orbit Control in Low Earth Orbit;                                 5th International Conference on Spacecraft Formation Flying Missions and Technologies, 29-31 May, 2013, Munich, Germany (2013);

D’Amico S., Bodin P., Delpech M., Noteborn R.;                                 PRISMA; Chap 21, pp. 599-637. In: D'Errico M. (Ed.)                                 Distributed Space Missions for Earth System Monitoring Space Technology Library, 2013, Volume 31, Part 4, 599-637.                                 DOI 10.1007/978-1-4614-4541-8_21

Montenbruck O., D'Amico S.;                                 GPS Based Relative Navigation;                                 Chap 5, pp. 185-223. In: D'Errico M. (Ed.)                                 Distributed Space Missions for Earth System Monitoring Space Technology Library, 2013, Volume 31, Part 2, 185-223.                                 DOI 10.1007/978-1-4614-4541-8_5

D’Amico S., Ardaens J.-S., Larsson R.                                 Spaceborne Autonomous Formation-Flying Experiment on the PRISMA Mission;                                 Journal of Guidance, Control, and Dynamics, vol.35, no.3, 834-850 (2012).                                 DOI 10.2514/1.55638

D’Amico S., Ardaens J.-S., De Florio S.                                 Autonomous formation flying based on GPS - PRISMA flight results;                                 Acta Astronautica (2012).                                 DOI 10.1016/j.actaastro.2012.04.033

Ardaens J.-S., D'Amico S. , Montenbruck O.;                                 Final commissioning of the PRISMA GPS navigation system;                                 Journal of Aerospace Engineering, Sciences and Applications, 4(3):104:118 (2012).

D’Amico S., Larsson R.;                                 Navigation and Control of the PRISMA Formation: In-Orbit Experience;                                 Journal of Mechanics Engineering and Automation, 2(5):312-320 (2012).

D’Amico S., Ardaens J.-S., Gaias G., Schlepp B., Benninghoff H., Tzschichholz T., Karlsson T., Jørgensen J. L.;                                 Flight Demonstration of Non-Cooperative Rendezvous using Optical Navigation;                                 23th International Symposium on Space Flight Dynamics, 29 October - 2 November, 2012, Pasadena, CA, USA (2012).

Ardaens J.-S., D'Amico S., Cropp A.;                                 GPS-Based Relative Navigation for the PROBA-3 Formation Flying Mission;                                 63rd International Astronautical Congress, 1-5 October, 2012, Naples (2012).

De Florio S., D'Amico S., and Radice G.;                                 Precise Autonomous Orbit Control in Low Earth Orbit;                                 AIAA/AAS Astrodynamics Specialist Conference, 13 - 16 August 2012, Minneapolis, MN, USA (2012).

Gaias G., D'Amico S., and Ardaens J.-S.;                                 Angles-only Navigation to a Non-Cooperative Satellite using Relative Orbital Elements;                                 AIAA/AAS Astrodynamics Specialist Conference, 13 - 16 August 2012, Minneapolis, MN, USA (2012).

De Florio S., D’Amico S., Radice G.;                                 Flight Results of the Precise Autonomous Orbit Keeping Experiment on the PRISMA Mission;                                 AAS 12-179; 22nd AAS/AIAA Space Flight Mechanics Meeting, 29 Jan. - 2 Feb. 2012, Charleston, SC (2012).

Bodin P., Noteborn R., Larsson R., Karlsson Th., D’Amico S., Ardaens J.-S., Delpech M., Berges J.-C.;                                 Prisma Formation Flying Demonstrator: Overview and Conclusions from the Nominal Mission;                                 AAS 12-072; 35th Annual AAS Guidance and Control Conference, 3-8 Feb. 2012, Breckenridge, Colorado (2012).

D'Amico S., Larsson R.;                                 Navigation and Control of the PRISMA Formation: In-Orbit Experience;                                 18th IFAC World Congress, 28 Aug.- 2Sep 2011, Milano, Italy (2011).

D'Amico S., Ardaens J.-S., Larsson R.;                                 Spaceborne Autonomous Formation Flying Experiment on the PRISMA Mission;                                 AIAA Guidance, Navigation, and Control Conference, 8-11 Aug. 2011, Portland, USA (2011).

Montenbruck O., D'Amico S., Ardaens J.-S., Wermuth M.;                                 Carrier Phase Differential GPS for LEO Formation Flying - The PRISMA and TanDEM-X Flight Experience;                                 AAS 11-489; AAS Astrodynamics Specialist Conference, 31 July - 4 Aug 2011, Girdwood, USA (2011).

Gaias G., Ardeans J.-S., D'Amico S.;                                 Formation Flying Testbed at DLR's German Space Operations Center;                                 8th International ESA Conference on Guidance, Navigation & Control Systems; 5-10 June 2010, Carlsbad, Czech Republic (2011).

Larsson R., Noteborn R., Bodin P., D'Amico S., Karlsson T., Carlsson A.;                                 Autonomous Formation Flying in LEO - Seven months of routine formation flying with frequent reconfigurations;                                 4th International Conference on Spacecraft Formation Flying Missions & Technologies; 18-20 May 2011, St-Hubert, Quebec (2011).

Larsson R., D'Amico S., Noteborn R., Bodin P.;                                 GPS Navigation Based Proximity Operations by the PRISMA Satellites - Flight Results;                                 4th International Conference on Spacecraft Formation Flying Missions & Technologies; 18-20 May 2011, St-Hubert, Quebec (2011).

D'Amico S., Ardaens J.-S., Larsson R.;                                 In-Flight Demonstration of Formation Control based on Relative Orbital Elements;                                 4th International Conference on Spacecraft Formation Flying Missions & Technologies; 18-20 May 2011, St-Hubert, Quebec (2011).

Ardaens J.S., D'Amico S., Fischer D.;                                 Early Flight Results from the TanDEM-X Autonomous Formation Flying System;                                 4th International Conference on Spacecraft Formation Flying Missions & Technologies; 18-20 May 2011, St-Hubert, Quebec (2011).

De Florio S., D'Amico S., Radice G.;                                 Operation Concept of the Precise Autonomous Orbit Keeping Experiment on the PRISMA Mission;                                 8th IAA Symposium on Small Satellites for Earth Observation, 4-8 April 2011, Berlin (2011).

Spurmann J., D'Amico S.;                                 Proximity Operations of On-Orbit Servicing Spacecraft using an Eccentricity/Inclination Vector Separation;                                 22nd International Symposium on Spaceflight Dynamics; 28 Feb. - 4 March 2011, Sao Jose dos Campos, Brazil (2011).

Ardaens J.-S., D'Amico S., Montenbruck O.;                                 Final Commissioning of the PRISMA GPS Navigation System;                                 22nd International Symposium on Spaceflight Dynamics; 28 Feb. - 4 March 2011, Sao Jose dos Campos, Brazil (2011).

D'Amico S.;                                 Autonomous formation flying in low earth orbit;                                 PhD thesis; Technical University of Delft (2010).

D’Amico S., Montenbruck O.;                                 Differential GPS: An Enabling Technology for Formation Flying Satellites;                                 in: Sandau R., Röser H.-P., Valenzuela A.;                                 Small Satellite Missions for Earth Observation;                                 pp. 457-466; Springer Verlag (2010).                                 DOI 10.1007/978-3-642-03501-2_43

Perea L., D'Amico S., Ardaens J.S., Elosegui P.;                                 Relative Control of a Virtual Telescope in a High Elliptical Orbit using GNSS and Optical Metrology;                                 Journal of Guidance, Control and Dynamics, 33/4, 1281-1287 (2010).                                 DOI 10.2514/1.48287

Ardaens J.-S., D'Amico S., Montenbruck O.;                                 Flight Results from the PRISMA GPS-Based Navigation;                                 5th ESA Workshop on Satellite Navigation Technologies, NAVITEC'2010, 8-10 Dec. 2010, Noordwijk, Netherlands (2010).

D'Amico S., Ardaens J.-S., DeFlorio S.;                                 Autonomous Formation Flying Based on GPS - PRISMA Flight Results;                                 6th International Workshop on Satellite Constellation and Formation Flying, 1-3 Nov. 2010, Taipei, Taiwan (2010).

Gaias G., D'Amico, Boge T.;                                 Hardware-in-the-loop Multi-satellite Simulator for Proximity Operations;                                 11th Int. WS on Simulation & EGSE facilities for Space Programmes; SESP 2010, 28-30 September 2010, Noordwijk, Netherlands.

Persson S., D'Amico S., Harr J.;                                 Flight Results from PRISMA Formation Flying and Rendezvous Demonstration Mission;                                 IAC-10-B4.2.9; 61st International Astronautical Congress, 27 Sep - 1 Oct 2010,Prague, CZ. (2010).

De Florio S., D'Amico S., Ardaens J.-S.;                                 Autonomous Navigation and Control of Formation Fliyng Spacecraft on the PRISMA Mission;                                 IAC-10.C1.5.12; 61st International Astronautical Congress, 27 Sep - 1 Oct 2010,Prague, CZ. (2010).

D'Amico S., Ardaens J.S., De Florio S., Montenbruck O., Persson S., Noteborn R.;                                 GPS-Based Spaceborne Autonomous Formation Flying Experiment (SAFE) on PRISMA: Initial Commissioning;                                 AIAA/AAS Astrodynamics Specialist Conference, 2-5 August 2010, Toronto, Canada.

Ardaens J.S., Montenbruck O., D'Amico S.;                                 Functional and Performance Validation of the PRISMA Precise Orbit Determination Facility;                                 ION International Technical Meeting, 25-27 Jan. 2010, San Diego, California (2010).

Persson S., Harr J., D'Amico S., Jörgensen J.;                                 PRISMA - Formation Flying Project Close to Launch;                                 Small Satellites Systems and Services- The 4S Symposium, 31 May - 4 June 2010, Madeira, Portugal (2010).

Ardaens J.-S., D'Amico S.;                                 Spaceborne Autonomous Relative Control System for Dual Satellite Formations;                                 Journal of Guidance, Control and Dynamics 32(6):1859-1870 (2009).                                 DOI 10.2514/1.42855

Delong N., Laurichesse D., Harr J., D'Amico S.;                                 PRISMA Relative Orbit Determination using GPS Measurements;                                 21st International Symposium on Space Flight Dynamics, 28 Sep. -2 Oct. 2009, Toulouse, France (2009).

L. Perea, S. D’Amico, P. Elosegui;                                 Relative Orbit Control of a Virtual Telescope in an Eccentric Orbit;                                 21st International Symposium on Space Flight Dynamics, 28 Sep. -2 Oct. 2009, Toulouse, France (2009).

D’Amico S., De Florio S., Larsson R., Nylund M.;                                 Autonomous Formation Keeping and Reconfiguration for Remote Sensing Spacecraft;                                 21st International Symposium on Space Flight Dynamics, 28 Sep. -2 Oct. 2009, Toulouse, France (2009).

D’Amico S., Ardaens J.-S., Montenbruck O.;                                 Navigation of Formation Flying Spacecraft using GPS: the PRISMA Technology Demonstration;                                 ION-GNSS-2009, 22 Sep. - 25 Sep. 2009, Savannah, USA (2009).

D’Amico S., Montenbruck O.;                                 Differential GPS: An Enabling Technology for Formation Flying Satellites;                                 7th IAA Symposium on Small Satellite for Earth Observation, 4-8 May 2009, Berlin (2009)

De Florio S., Gill E., D’Amico S.;                                 Performance Comparison of Microprocessors for Space-based Navigation Applications;                                 7th IAA Symposium on Small Satellite for Earth Observation, 4-8 May 2009, Berlin (2009)

De Florio S., D’Amico S.;                                 Optimal Autonomous Orbit Control of Remote Sensing Spacecraft;                                 19th AAS/AIAA Space Flight Mechanics Meeting, 8-12 Feb 2009, Savannah, USA (2009).

Ardaens J.-S., D'Amico S.;                                 Formation Flying Testbed;                                 DLR-GSOC TN 09-01; Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen (2009).

De Florio S., D'Amico S.;                                 The Precise Autonomous Orbit Keeping Experiment on the PRISMA Mission;                                 Journal of the Astronautical Sciences 56(4), 477-494 (2009).

Montenbruck O., Kahle R., D'Amico S., Ardaens J.-S.;                                 Navigation and Control of the TanDEM-X Formation;                                 Journal of the Astronautical Sciences, 56(3):341-357 (2008).

Yamamoto T., D'Amico S.;                                 Hardware-in-the-loop Demonstration of GPS-Based Autonomous Formation Flying;                                 4th ESA Workshop on Satellite Navigation User Equipment Technologies, NAVITEC'2008, 10-12 December 2008, Noordwijk (2008).

Montenbruck O., Delpech M., Ardaens J.-S., Delong N., D'Amico S., Harr J.;                                 Cross-Validation of GPS and FFRF-Based Relative Navigation for the PRISMA Mission;                                 4th ESA Workshop on Satellite Navigation User Equipment Technologies, NAVITEC'2008, 10-12 December 2008, Noordwijk (2008).

D'Amico S., Montenbruck O., Larsson R., Chasset C.;                                 GPS-Based Relative Navigation during the Separation Sequence of the PRISMA Mission;                                 AIAA-2008-6661; AIAA Guidance, Navigation and Control Conference, 18-21 Aug. 2008, Honolulu, Hawaii (2008).

D'Amico S., Ardaens J.-S., De Florio S., Montenbruck O.;                                 Autonomous Formation Flying - TanDEM-X, PRISMA and Beyond;                                 5th International Workshop on Satellite Constellations & Formation Flying, 2-4 July 08, Evpatoria, Crimea (2008).

D'Amico S., De Florio S., Ardaens J. S., Yamamoto T.;                                 Offline and Hardware-in-the-loop Validation of the GPS-based Real-Time Navigation System for the PRISMA Formation Flying Mission;                                 3rd International Symposium on Formation Flying, Missions and Technology, 23-25 April 2008, ESA/ESTEC, Noordwijk (2008).

Ardaens J.-S., D'Amico S., Ulrich D., Fischer D.;                                 TanDEM-X Autonomous Formation Flying System;                                 3rd International Symposium on Formation Flying, Missions and Technology, 23-25 April 2008, ESA/ESTEC, Noordwijk (2008).

De Florio S., D'Amico S., Gracia-Fernandez M.;                                 The Precise Autonomous Orbit Keeping Experiment on the PRISMA Formation Flying Mission;                                 AAS 08-212; 18th AAS/AIAA Space Flight Mechanics Meeting, 28-31 Jan. 2008, Galveston, Texas (2008).

Ardaens J.S., D’Amico S.;                                 Control of Formation Flying Spacecraft at a Lagrange Point;                                 DLR-GSOC TN 08-01; Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen (2007).

Gill E., D'Amico S., Montenbruck O.;                                 Autonomous Formation Flying for the PRISMA Mission;                                 AIAA Journal of Spacecraft and Rockets, 44/3, 671-681 (2007).                                 DOI 10.2514/1.23015

Rupp T., D'Amico S., Montenbruck O., Gill E.;                                 Autonomous Formation Flying at DLR's German Space Operations Center (GSOC);                                 IAC-07-D1.2; 58th International Astronautical Congress; 24-28 Sept. 2007; Hyderabad, India (2007).

Ardaens J.S., D’Amico S., Kazeminejad B., Montenbruck O., Gill E.;                                 Spaceborne Autonomous and Ground Based Relative Orbit Control for the TerraSAR-X/TanDEM-X Formation;                                 20th International Symposium on Space Flight Dynamics; 24-28 Sep. 2007, Annapolis, USA (2007).

Kahle R., Kazeminejad B., Kirschner M., Yoon Y., Kiehling R., D’Amico S.;                                 First In-orbit Experience of TerraSAR-X Flight Dynamics Operations;                                 20th International Symposium on Space Flight Dynamics; 24-28 Sep. 2007, Annapolis, USA (2007).

Montenbruck O., Kirschner M., D'Amico S., Bettadpur S.;                                 E/I-Vector Separation for Safe Switching of the GRACE Formation;                                 Aerospace Science and Technology 10/7, 628-635 (2006).                                 DOI 10.1016/j.ast.2006.04.001

Kohlhase A.O., Kroes R., D'Amico S.;                                 Interferometric baseline performance estimations for multistatic SAR configurations derived from GRACE GPS observations;                                 Journal of Geodesy 80/1, 28-39 (2006).                                 DOI 10.1007/s00190-006-0027-y

D'Amico S., Montenbruck O.;                                 Proximity Operations of Formation Flying Spacecraft using an Eccentricity/Inclination Vector Separation;                                 AIAA Journal of Guidance, Control and Dynamics, Vol. 29, No. 3, 554-563 (2006).

Enderle W., Fiedler H., De Florio S., Krieger G., Jochim F., D'Amico S., Dawson S., Kellar W.;                                 Next Generation GNSS for Navigation of Future SAR Constellations;                                 IAC-06-C1.8.06; 57th International Astronautical Congress, Oct. 2-6, 2006, Valencia, Spain (2006).

D'Amico S., Gill E., Montenbruck O..;                                 Relative Orbit Control Design for the PRISMA Formation Flying Mission;                                 AIAA Guidance, Navigation, and Control Conference, 21-24 Aug. 2006, Keystone, Colorado (2006).

D'Amico S., Gill E., Garcia M., Montenbruck O., Gill E.;                                 GPS-Based Real-Time Navigation for the PRISMA Formation Flying Mission;                                 3rd ESA Workshop on Satellite Navigation User Equipment Technologies, NAVITEC'2006, 11-13 December 2006, Noordwijk (2006).

Kohlhase A., Kroes R., D'Amico S.;                                 Evaluating interferometric baseline performances in a close formation flight by using relative GRACE GPS navigation solutions;                                 19th International Symposium on Space Flight Dynamics; 4-11 June 2006, Kanazawa, Japan (2006).

Fiedler H., Krieger G., Werner M., Reiniger K., Diedrich E., Eineder M., D’Amico S., Riegger S.;                                 The TanDEM-X Mission Design and Data Acquisition Plan;                                 6th European Conference on Synthectic Aperture Radar, 16-18 May 2006, Dresden, Germany (2006).

Gill E., Montenbruck O., D'Amico S., Persson S.;                                 Autonomous Satellite Formation Flying for the PRISMA Technology Demonstration Mission;                                 16th AAS/AIAA Space Flight Mechanics Conference, Jan. 22-26, 2006, Tampa, Florida (2006).

D’Amico S., Montenbruck O., Arbinger Ch., Fiedler H.;                                 Formation Flying Concept for Close Remote Sensing Satellites;                                 AAS 05-156; 15th AAS/AIAA Space Flight Mechanics Conference; Jan. 23-27, 2005; Copper Mountain, Colorado (2005).

D'Amico S.;                                 Relative Orbital Elements as Integration Constants of the Hill’s Equations;                                 DLR-GSOC TN 05-08; Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen (2005).

D'Amico S.;                                 Flight Dynamics Operations for TanDEM-X Formation Flying;                                 DLR-GSOC TN 05-03; Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen (2005).

D’Amico S., Arbinger Ch., Kirschner M., Campagnola S.;                                 Generation of an Optimum Target Trajectory for the TerraSAR-X Repeat Observation Satellite;                                 18th International Symposium on Space Flight Dynamics, 11-15 Oct. 2004, Munich, Germany (2004).

Arbinger Ch., D’Amico S., Eineder M.;                                 Precise Ground-In-the-Loop Orbit Control for Low Earth Observation Satellites;                                 18th International Symposium on Space Flight Dynamics, 11-15 Oct. 2004, Munich, Germany (2004).

Arbinger Ch., D’Amico S.;                                 Impact of Orbit Prediction Accuracy on Low Earth Remote Sensing Flight Dynamics Operations;                                 18th International Symposium on Space Flight Dynamics, 11-15 Oct. 2004, Munich, Germany (2004).

Montenbruck O., D’Amico S.;                                 Space Segment Requirements for TanDEM-X Formation Flying;                                 DLR-GSOC TN 04-09; Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen (2004).

Montenbruck O., Kirschner M., D’Amico S.;                                 E/I-Vector Separation for GRACE Proximity Operations;                                 DLR-GSOC TN 04-08; Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen (2004).

Kirschner M., D’Amico S.;                                 Generation of the TerraSar-X Reference Orbit ;                                 DLR-GSOC TN 04-06; Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen (2004).

D’Amico S., Kirschner M., Arbinger Ch.;                                 Precise Orbit Control of LEO Repeat Observation Satellites with Ground-In-The-Loop;                                 DLR-GSOC TN 04-05; Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen (2004).

Arbinger Ch., D'Amico S., Feucht U., Finzi A.;                                 The GRACE Formation: Science Mode Pointing Performance Analysis;                                 3rd International Workshop on Satellite Constellations and Formations, 24-26 Feb. 2003, Pisa (2003).

D'Amico S.;                                 Attitude and orbital simulation in support of space mission operations: the GRACE formation flying;                                 Master Thesis, Politecnico di Milano (2002).

DARPA's autonomous 'Manta Ray' drone can glide through ocean depths undetected

Northrop Grumman Corporation has built its Manta Ray uncrewed underwater vehicle, which will operate long-duration missions and carry payloads into the ocean depths in partnership with DARPA.

Engineers have assembled an autonomous underwater drone that the U.S. Defense Advanced Research Projects Agency (DARPA) plans to use for long-range missions in the ocean, pictures show.

Dubbed "Manta Ray," the drone is modeled after filter-feeding fish of the same name with diamond-shaped bodies and wing-like fins. The prototype, designed and built by the aerospace and defense company Northrop Grumman , is an extra-large glider capable of operating long-duration, payloaded missions without needing on-board human support and maintenance. 

Once deployed, the drone could also save energy by anchoring itself to the seabed and hibernating in a low-power mode.

Related: Superfast drone fitted with new 'rotating detonation rocket engine' approaches the speed of sound

"I’m excited to repost one of the first pictures of Manta Ray fully assembled in our Annapolis facility," Todd Leavitt, the vice president of naval and oceanic systems at Northrop Grumman, wrote in a LinkedIn post resharing the company's announcement.

DARPA launched the Manta Ray program in 2020 to improve underwater vehicle design, including developing techniques to increase payload capacity and conserve energy. The agency initially selected three contractors — Northrop Grumman, Martin Defense Group LLC and Metron Inc. — but the latter dropped out in late 2021. Since then, Northrop Grumman and Martin Defense Group LLC have each developed unique prototypes of the drone for in-water demonstrations and testing.

— DARPA picks Northrop Grumman to develop 'lunar raiload' concept

—  DARPA is exploring ways to build big things in space

—  DARPA-funded 'Inchworm' robots could help us build moon bases

Northrop Grumman's Manta Ray prototype integrates several unique features to support DARPA's vision of creating "strategic surprise," the company said on its website . These include autonomy, carrying capacity for equipment to support different missions, energy-saving functions and modularity — meaning the design can be taken apart and reassembled. The drone also fits into five standard shipping containers, meaning it can be transported and deployed worldwide.

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Manta Ray could become a critical tool for underwater military operations and "is uniquely positioning itself to simultaneously introduce a new class of underwater vehicle while contributing key component technologies to other vital undersea programs," Kyle Woerner , the program manager for Manta Ray at DARPA, said in a 2021 statement . 

Join our Space Forums to keep talking space on the latest missions, night sky and more! And if you have a news tip, correction or comment, let us know at: [email protected].

Sascha is a U.K.-based trainee staff writer at Live Science. She holds a bachelor’s degree in biology from the University of Southampton in England and a master’s degree in science communication from Imperial College London. Her work has appeared in The Guardian and the health website Zoe. Besides writing, she enjoys playing tennis, bread-making and browsing second-hand shops for hidden gems.

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Offline Learning for Stochastic Multi-Agent Planning in Autonomous Driving

Fully autonomous vehicles have the potential to greatly reduce vehicular accidents and revolutionize how people travel and how we transport goods. Many of the major challenges for autonomous driving systems emerge from the numerous traffic situations that require complex interactions with other agents. For the foreseeable future, autonomous vehicles will have to share the road with human drivers and pedestrians, and thus cannot rely on centralized communication to address these interactive scenarios. Therefore, autonomous driving systems need to be able to negotiate and respond to unknown agents that exhibit uncer?tain behavior. To tackle these problems, most commercial autonomous driving stacks use a modular approach that splits perception, agent forecasting, and planning into separately engineered modules. However, fully separating prediction and planning makes it difficult to reason how other vehicles will respond to the planned trajectory for the controlled ego-vehicle. So to maintain safety, many modular approaches have to be overly conservative when interacting with other agents. Ideally, we want autonomous vehicles to drive in a natural and confident manner, while still maintaining safety. 

Thus, in this thesis, we will explore how we can use deep learning and offline reinforce?ment learning to perform joint prediction and planning in highly interactive and stochastic multi-agent scenarios in autonomous driving. First, we discuss our work in using deep learning for joint prediction and closed-loop planning in an offline reinforcement learning (RL) framework (Chapter 2). Second, we discuss our work that directly tackles the difficulties of using learned models to do planning in stochastic multimodal settings (Chapter 3). Third, we discuss how we can scale to more complicated multi-agent driving scenarios like merging in dense traffic by using a Transformer-based traffic forecasting model as our world model (Chapter 4). Finally, we discuss how we can draw from offline model-based RL to learn a high-level policy that selects over a discrete set of pre-trained driving skills to perform effective control without additional online planning (Chapter 5). 

Degree Type

• Dissertation
• Robotics Institute

Degree Name

• Doctor of Philosophy (PhD)

Usage metrics

• Adaptive Agents and Intelligent Robotics

What fire ants can teach us about making better, self-healing materials

Research explores how ant 'rafts' bind together to survive flooding.

Fire ants form rafts to survive flooding, but how do those bonds work? And what can we learn from them? A Binghamton University, State University of New York professor is researching those questions to expand our knowledge of materials science.

When flooding hits a region where fire ants live, their survival response is to latch together to form a buoyant "raft" that floats and keeps the colony united. Think of it like a condensed, adaptive material where the building blocks -- individual ants -- are actually alive.

Binghamton University Assistant Professor Rob Wagner led research as part of the Vernerey Soft Matter Mechanics Lab at University of Colorado Boulder in which they investigated the adaptive response of these living rafts. The goals are to understand how they autonomously morph and change their mechanical properties, and then incorporate the simplest and most useful discoveries into artificial materials.

"Living systems have always fascinated me, because they achieve things that our current engineered materials cannot -- not even close," he said. "We manufacture bulk polymeric systems, metals and ceramics, but they're passive. The constituents don't store energy and then convert it to mechanical work the way every single living system does."

Wagner sees this storage and conversion of energy as essential to mimicking the smart and adaptive behaviors of living systems.

In their most recent publication in the Proceedings of the National Academy of Sciences , Wagner and his co-authors at University of Colorado investigated how fire ant rafts responded to mechanical load when stretched, and they compared the response of these rafts to dynamic, self-healing polymers.

"Many polymers are held together by dynamic bonds that break, but can reform," Wagner said. "When pulled slowly enough, these bonds have time to restructure the material so that -- instead of fracturing -- it flows like the slime our kids play with, or soft-serve ice cream. When pulled very fast, though, it breaks more like chalk. Since the rafts are held together by ants clinging onto one another, their bonds can break and reform. So, my colleagues and I thought they'd do the same thing."

But Wagner and his collaborators discovered that no matter what speed they pulled the ant rafts, their mechanical response was nearly the same, and they never flowed. Wagner speculates that the ants reflexively tighten and prolong their holds when they feel force because they want to stay together. They either turn down or turn off their dynamic behavior.

This phenomenon of bonds that grow stronger when force is applied to them is called catch bond behavior, and it likely enhances cohesion for the colony, which makes sense for survival.

"As you pull on typical bonds with some amount of force, they're going to let go sooner, and their lifetime goes down -- you're weakening the bond by pulling on it. That is what you see in almost any passive system," Wagner said. "But in living systems, because of their complexity, you can sometimes have catch bonds that hold on for longer durations under some range of applied force. Some proteins do this mechanistically and automatically, but it's not like the proteins are making a decision. They're just arranged in such a way that when a force is applied, it reveals these binding sites that latch or 'catch'."

Wagner believes that mimicking these catch bonds in engineered systems could lead to artificial materials that exhibit autonomous, localized self-strengthening in regions of higher mechanical stress. This could enhance the lifetimes of biomedical implants, adhesives, fiber composites, soft robotics components and many other systems.

Collective insect aggregations like fire ant rafts already are inspiring researchers to develop materials with stimuli-responsive mechanical properties and behaviors. A paper in Nature Materials earlier this year -- led by the Ware Responsive Biomaterials Lab at Texas A&M and including contributions from Wagner and his former thesis advisor, Professor Franck J. Vernerey -- demonstrates how ribbons made of special gels or materials called liquid crystal elastomers can coil due to heating, and then entangle with each other to form condensed, solid-like structures that were inspired by these ants

"A natural progression of this work is to answer how we can get the interactions between these ribbons or other soft building blocks to 'catch' under load like the fire ants and some biomolecular interactions do," Wagner said.

• Invasive Species
• Insects (including Butterflies)
• Biotechnology and Bioengineering
• Materials Science
• Engineering and Construction
• Civil Engineering
• Thermodynamics
• Materials science
• Bird intelligence
• Fire fighting

Story Source:

Materials provided by Binghamton University . Original written by Chris Kocher. Note: Content may be edited for style and length.

Journal Reference :

• Robert J. Wagner, Samuel C. Lamont, Zachary T. White, Franck J. Vernerey. Catch bond kinetics are instrumental to cohesion of fire ant rafts under load . Proceedings of the National Academy of Sciences , 2024; 121 (17) DOI: 10.1073/pnas.2314772121

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