arXiv subjects

Sean Phillips

Publications and source records attributed to Sean Phillips.

At least 19 recordsLinked to original sources

arXiv2026

Information-Aware Model Predictive Control for Satellite Inspection

Autonomous spacecraft inspection requires trajectories that satisfy safety and control constraints while enabling the collection of informative measurements about a target spacecraft. Traditional guidance and control methods typically decouple estimation from control, resulting in trajectories that do not explicitly optimize sensing geometry. This work presents a model predictive control (MPC) framework that incorporates estimation covariance in the control objective using a formulation inspired by dual control and covariance steering. The estimation covariance evolves according to a linear Kalman filter, and the measurement model depends on the relative geometry between the agent spacecraft and the target. By embedding the covariance dynamics within the MPC problem, the resulting trajectories account for measurement quality, actively reduce uncertainty, and improve observability in the estimated features of the target. The problem is formulated using relative motion dynamics via the Hill-Clohessy-Wiltshire equations with constraints on control input, relative distance, and terminal maximum covariance. Numerical simulations demonstrate that the proposed framework generates feasible inspection trajectories that actively reduce estimation covariance of points of interest on a target while satisfying input and safety constraints of the agent. A mesh analysis of initial conditions further illustrates how feasibility and the value function of the trajectory depend on the initial conditions and constraint activity.

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arXiv2026

Autonomous Satellite Rendezvous via Hybrid Feedback Optimization

As satellites have proliferated, interest has increased in autonomous rendezvous, proximity operations, and docking (ARPOD). A fundamental challenge in these tasks is the uncertainties when operating in space, e.g., in measurements of satellites' states, which can make future states difficult to predict. Another challenge is that satellites' onboard processors are typically much slower than their terrestrial counterparts. Therefore, to address these challenges we propose to solve an ARPOD problem with feedback optimization, which computes inputs to a system by measuring its outputs, feeding them into an optimization algorithm in the loop, and computing some number of iterations towards an optimal input. We focus on satellite rendezvous, and satellites' dynamics are modeled using the continuous-time Clohessy-Wiltshire equations, which are marginally stable. We develop an asymptotically stabilizing controller for them, and we use discrete-time gradient descent in the loop to compute inputs to them. Then, we analyze the hybrid feedback optimization system formed by the stabilized Clohessy-Wiltshire equations with gradient descent in the loop. We show that this model is well-posed and that maximal solutions are both complete and non-Zeno. Then, we show that solutions converge exponentially fast to a ball around a rendezvous point, and we bound the radius of that ball in terms of system parameters. Simulations show that this approach provides up to a 98.4\% reduction in the magnitude of disturbances across a range of simulations, which illustrates the viability of hybrid feedback optimization for autonomous satellite rendezvous.

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arXiv2025

Autonomy at Levels for Spacecraft

Autonomy at Levels is the idea that autonomy should be embedded within and throughout a spacecraft. Using Systems Engineering methods a spacecraft is typically decomposed into systems, subsystems, assemblies, components, and so on. All these decomposition levels within all the spacecraft's systems, could and should have autonomy elements built in. As a result, the "autonomy system" is made of autonomy elements or units that are integrated, distributed and embedded within the whole spacecraft. This is like how the power system would be designed and implemented. Linking control loops and autonomy loops illustrates how to achieve Autonomy at Levels.

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arXiv2025

Sensor Fusion Methods for Gaussian Mixture Models

Consensus is a popular technique for distributed state estimation. This formulation allows networks of connected agents or sensors to exchange information about the distribution of a set of targets with their immediate neighbors without the need of a centralized node or layer. We present decentralized consensus-based fusion techniques for a system whose target prior estimates are a weighted mixture of Gaussian probability density functions (PDFs) for the following cases: 1) in which all agents have the same a priori Gaussian mixture estimate of the target, and 2) in which agents have different a priori Gaussian mixture estimates of the target. For the second case, we present a formulation that fuses each agent's a priori estimate without using local observations such that each agent's posterior estimate is the same across the network.

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arXiv2025

ChronoSync: A Decentralized Chronometer Synchronization Protocol for Multi-Agent Systems

This work presents a decentralized time synchronization algorithm for multi-agent systems. Each agent possesses two clocks, a hardware clock that is perturbed by environmental phenomena (e.g., temperature, humidity, pressure, g forces, etc.) and a steerable software clock that inherits the perturbations affecting the hardware clock. Under these disturbances and the independent time kept by the hardware clocks, our consensus-based controller enables all agents to steer their software-defined clocks into practical synchronization while achieving a common user-defined clock drift. Furthermore, we treat the drift of each hardware clock as an unknown parameter, which our algorithm can accurately estimate. The coupling of the agents is modeled by a connected, undirected, and static graph. However, each agent possesses a timer mechanism that determines when to broadcast a sample of its software time and update its own software-time estimate. Hence, communication between agents can be directed, intermittent, and asynchronous. The closed-loop dynamics of the ensemble is modeled using a hybrid system, where a Lyapunov-based stability analysis demonstrates that a set encoding the time synchronization and clock drift estimation objectives is globally practically exponentially stable. The performance suggested by the theoretical development is confirmed in simulation.

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arXiv2025

Spacecraft Autonomy Levels

As autonomous systems are being developed in multiple industries it has been recognized that a phased approach is needed both for technical development and user acceptance. Partially in response, the automotive and aircraft industries have published five or six Levels of Autonomy in an attempt to broadly characterize the amount of autonomy technology that is present in any particular vehicle. We propose six Spacecraft Autonomy Levels to characterize and describe the high-level autonomous capabilities for any given satellite. Our proposed spacecraft autonomy levels parallel those of the automotive and aircraft levels in an attempt to maintain consistency and a common understanding. We see these autonomy levels as useful in education and communication with lawmakers, government officials, and the general public.

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arXiv2025

Levels of Spacecraft Autonomy

As autonomous systems are being developed in multiple industries it has been recognized that a phased approach is needed both for technical development and user acceptance. Partially in response, the automotive and aircraft industries have published five or six Levels of Autonomy in an attempt to broadly characterize the amount of autonomy technology that is present in any particular vehicle. We have developed a similar six Levels of Spacecraft Autonomy for similar purposes. Here, we propose extending these ideas to spacecraft constellations, clusters, swarms, ground stations for spacecraft, and strategic collaboration among space and ground assets. We intend these levels of autonomy to be used to characterize and describe high-level capabilities for any given satellite for education and communication with lawmakers, government officials, and the general public. All of the proposed levels mimic those of the automotive and aircraft levels in an attempt to maintain consistency and a common understanding with our intended audiences.

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arXiv2025

Close-Proximity Satellite Operations through Deep Reinforcement Learning and Terrestrial Testing Environments

With the increasingly congested and contested space environment, safe and effective satellite operation has become increasingly challenging. As a result, there is growing interest in autonomous satellite capabilities, with common machine learning techniques gaining attention for their potential to address complex decision-making in the space domain. However, the "black-box" nature of many of these methods results in difficulty understanding the model's input/output relationship and more specifically its sensitivity to environmental disturbances, sensor noise, and control intervention. This paper explores the use of Deep Reinforcement Learning (DRL) for satellite control in multi-agent inspection tasks. The Local Intelligent Network of Collaborative Satellites (LINCS) Lab is used to test the performance of these control algorithms across different environments, from simulations to real-world quadrotor UAV hardware, with a particular focus on understanding their behavior and potential degradation in performance when deployed beyond the training environment.

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arXiv2025

Assessing Autonomous Inspection Regimes: Active Versus Passive Satellite Inspection

This paper addresses the problem of satellite inspection, where one or more satellites (inspectors) are tasked with imaging or inspecting a resident space object (RSO) due to potential malfunctions or anomalies. Inspection strategies are often reduced to a discretized action space with predefined waypoints, facilitating tractability in both classical optimization and machine learning based approaches. However, this discretization can lead to suboptimal guidance in certain scenarios. This study presents a comparative simulation to explore the tradeoffs of passive versus active strategies in multi-agent missions. Key factors considered include RSO dynamic mode, state uncertainty, unmodeled entrance criteria, and inspector motion types. The evaluation is conducted with a focus on fuel utilization and surface coverage. Building on a Monte-Carlo based evaluator of passive strategies and a reinforcement learning framework for training active inspection policies, this study investigates conditions under which passive strategies, such as Natural Motion Circumnavigation (NMC), may perform comparably to active strategies like Reinforcement Learning based waypoint transfers.

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arXiv2025

Stability Analysis of Deep Reinforcement Learning for Multi-Agent Inspection in a Terrestrial Testbed

The design and deployment of autonomous systems for space missions require robust solutions to navigate strict reliability constraints, extended operational duration, and communication challenges. This study evaluates the stability and performance of a hierarchical deep reinforcement learning (DRL) framework designed for multi-agent satellite inspection tasks. The proposed framework integrates a high-level guidance policy with a low-level motion controller, enabling scalable task allocation and efficient trajectory execution. Experiments conducted on the Local Intelligent Network of Collaborative Satellites (LINCS) testbed assess the framework's performance under varying levels of fidelity, from simulated environments to a cyber-physical testbed. Key metrics, including task completion rate, distance traveled, and fuel consumption, highlight the framework's robustness and adaptability despite real-world uncertainties such as sensor noise, dynamic perturbations, and runtime assurance (RTA) constraints. The results demonstrate that the hierarchical controller effectively bridges the sim-to-real gap, maintaining high task completion rates while adapting to the complexities of real-world environments. These findings validate the framework's potential for enabling autonomous satellite operations in future space missions.

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arXiv2025

Time-Constrained Model Predictive Control for Autonomous Satellite Rendezvous, Proximity Operations, and Docking

This paper presents a time-constrained model predictive control strategy for the six degree-of-freedom autonomous rendezvous, proximity, operations and docking problem between a controllable "deputy" satellite and an uncontrolled "chief" satellite. The objective is to achieve a docking configuration defined by both the translational and attitudinal states of the deputy relative to the chief, whose dynamics are respectively governed by both the Clohessy-Wiltshire equations and Euler's second law of motion. The proposed control strategy explicitly addresses computational time constraints that are common to state-of-the-art space vehicles. Thus, a time-constrained model predictive control strategy is implemented on a space-grade processor. Although suboptimal with regards to energy consumption when compared to conventional optimal RPO trajectories, it is empirically demonstrated via numerical simulations that the deputy spacecraft still achieves a successful docking configuration while subject to computational time constraints.

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arXiv2025

The Safe Trusted Autonomy for Responsible Space Program

The Safe Trusted Autonomy for Responsible Space (STARS) program aims to advance autonomy technologies for space by leveraging machine learning technologies while mitigating barriers to trust, such as uncertainty, opaqueness, brittleness, and inflexibility. This paper presents the achievements and lessons learned from the STARS program in integrating reinforcement learning-based multi-satellite control, run time assurance approaches, and flexible human-autonomy teaming interfaces, into a new integrated testing environment for collaborative autonomous satellite systems. The primary results describe analysis of the reinforcement learning multi-satellite control and run time assurance algorithms. These algorithms are integrated into a prototype human-autonomy interface using best practices from human-autonomy trust literature, however detailed analysis of the effectiveness is left to future work. References are provided with additional detailed results of individual experiments.

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arXiv2025

Collaborative Spacecraft Servicing under Partial Feedback using Lyapunov-based Deep Neural Networks

Multi-agent systems are increasingly applied in space missions, including distributed space systems, resilient constellations, and autonomous rendezvous and docking operations. A critical emerging application is collaborative spacecraft servicing, which encompasses on-orbit maintenance, space debris removal, and swarm-based satellite repositioning. These missions involve servicing spacecraft interacting with malfunctioning or defunct spacecraft under challenging conditions, such as limited state information, measurement inaccuracies, and erratic target behaviors. Existing approaches often rely on assumptions of full state knowledge or single-integrator dynamics, which are impractical for real-world applications involving second-order spacecraft dynamics. This work addresses these challenges by developing a distributed state estimation and tracking framework that requires only relative position measurements and operates under partial state information. A novel $\rho$-filter is introduced to reconstruct unknown states using locally available information, and a Lyapunov-based deep neural network adaptive controller is developed that adaptively compensates for uncertainties stemming from unknown spacecraft dynamics. To ensure the collaborative spacecraft regulation problem is well-posed, a trackability condition is defined. A Lyapunov-based stability analysis is provided to ensure exponential convergence of errors in state estimation and spacecraft regulation to a neighborhood of the origin under the trackability condition. The developed method eliminates the need for expensive velocity sensors or extensive pre-training, offering a practical and robust solution for spacecraft servicing in complex, dynamic environments.

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arXiv2024

Consensus over Clustered Networks Using Intermittent and Asynchronous Output Feedback

Distributed consensus protocols provide a mechanism for spreading information within clustered networks, allowing agents and clusters to make decisions without requiring direct access to the state of the ensemble. In this work, we propose a strategy for achieving system-wide consensus in the states of identical linear time-invariant systems coupled by an undirected graph whose directed sub-graphs are available only at sporadic times. Within this work, the agents of the network are organized into pairwise disjoint clusters, which induce sub-graphs of the undirected parent graph. Some cluster sub-graph pairs are linked by an inter-cluster sub-graph, where the union of all cluster and inter-cluster sub-graphs yields the undirected parent graph. Each agent utilizes a distributed consensus protocol with components that are updated intermittently and asynchronously with respect to other agents and inter-clusters. The closed-loop ensemble dynamics is modeled as a hybrid system, and a Lyapunov-based stability analysis yields sufficient conditions for rendering the agreement subspace (consensus set) globally exponentially stable. Furthermore, an input-to-state stability argument demonstrates the consensus set is robust to a large class of perturbations. A numerical simulation considering both nominal and perturbed scenarios is provided for validation purposes.

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arXiv2024

Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehicles

On-orbit spacecraft inspection is an important capability for enabling servicing and manufacturing missions and extending the life of spacecraft. However, as space operations become increasingly more common and complex, autonomous control methods are needed to reduce the burden on operators to individually monitor each mission. In order for autonomous control methods to be used in space, they must exhibit safe behavior that demonstrates robustness to real world disturbances and uncertainty. In this paper, neural network controllers (NNCs) trained with reinforcement learning are used to solve an inspection task, which is a foundational capability for servicing missions. Run time assurance (RTA) is used to assure safety of the NNC in real time, enforcing several different constraints on position and velocity. The NNC and RTA are tested in the real world using unmanned aerial vehicles designed to emulate spacecraft dynamics. The results show this emulation is a useful demonstration of the capability of the NNC and RTA, and the algorithms demonstrate robustness to real world disturbances.

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arXiv2024

Timer-Based Coverage Control for Mobile Sensors

This work investigates the coverage control problem over a static, compact, and convex workspace and develops a hybrid extension of the continuous-time Lloyd algorithm. Each agent in a multi-agent system (MAS) is equipped with a timer mechanism that generates intermittent measurement and control update events, which may occur asynchronously between agents. Between consecutive event times, as determined by the corresponding timer mechanism, the controller of each agent is held constant. These controllers are shown to drive the configuration of the MAS into a neighborhood of the set of centroidal Voronoi configurations, i.e., the minimizers of the standard locational cost. The combination of continuous-time dynamics with intermittently updated control inputs is modeled as a hybrid system. The coverage objective is posed as a set attractivity problem for hybrid systems, where an invariance-based convergence analysis yields sufficient conditions that ensure maximal solutions of the hybrid system asymptotically converge to a desired set. A brief simulation example is included to showcase the result.

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arXiv2023

Distributed Nonlinear Filtering using Triangular Transport Maps

The distributed filtering problem sequentially estimates a global state variable using observations from a network of local sensors with different measurement models. In this work, we introduce a novel methodology for distributed nonlinear filtering by combining techniques from transportation of measures, dimensionality reduction, and consensus algorithms. We illustrate our methodology on a satellite pose estimation problem from a network of direct and indirect observers. The numerical results serve as a proof of concept, offering new venues for theoretical and applied research in the domain of distributed filtering.

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arXiv2023

Computational Optimal Transport and Filtering on Riemannian manifolds

In this paper we extend recent developments in computational optimal transport to the setting of Riemannian manifolds. In particular, we show how to learn optimal transport maps from samples that relate probability distributions defined on manifolds. Specializing these maps for sampling conditional probability distributions provides an ensemble approach for solving nonlinear filtering problems defined on such geometries. The proposed computational methodology is illustrated with examples of transport and nonlinear filtering on Lie groups, including the circle $S^1$, the special Euclidean group $SE(2)$, and the special orthogonal group $SO(3)$.

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