Algorithmic Foundations for Real-Time and Dependable Spacecraft Motion Planning
The goal of this effort is to devise real-time, efficient and dependable algorithms for spacecraft autonomous maneuvering, with a focus on dynamic and cluttered environments (e.g., due to debris or outgassing activity). Specifically, this project is aimed at devising a technology for the online planning of trajectories in proximity operations, which together with reliable environmental sensing and autonomous high-level decision-making is a key enabler for autonomous spacecraft navigation (see Figure 1). As a radical departure from traditional methods, we are leveraging recent algorithmic advances in the field of robotic motion planning for autonomous driving to spacecraft control. Our research objectives are to:
Address the theoretical underpinnings for the application of robotic motion planning algorithms to the problem of onboard spacecraft maneuvering, with special attention paid to implementability on space-qualified hardware.
Integrate the planning module within the overall spacecraft autonomy module, with a focus on encoding safety modes and addressing environmental uncertainties.
Validate our algorithms on a state-of-the-art test bed that emulates both deep-space and microgravity environments.
This project involves collaborations with the NASA Goddard Space Flight Center and the NASA Jet Propulsion Laboratory. These collaborations are instrumental to a possible technology infusion in future NASA missions.
Spacecraft motion planning in a nutshell
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Autonomous spacecraft navigation and maneuvering is an enabling factor for a wide range of missions, ranging from on-orbit satellite servicing to operations in proximity of outgassing bodies (see Figure). Generally speaking, spacecraft autonomy entails reliable environmental sensing, autonomous high-level decision making, and online planning of trajectories. In this project we will focus on this last aspect, by devising real-time, provably efficient and dependable algorithms for spacecraft motion planning in dynamic and cluttered environments. |
Publications
Kinodynamic motion planning for drift systems: [Paper]
Kinodynamic motion planning for driftless systems: [Paper]
Bidirectional motion planning: [Paper]
A Machine learning approach for real-time reachability analysis: [Paper]
Fast Marching Trees: a fast marching sampling-based method for optimal motion planning in many dimensions [Paper]
Spacecraft autonomy challenges for next generation space missions [Paper]
Movies
Team
This project involves collaborations with the NASA Goddard Space Flight Center and the NASA Jet Propulsion Laboratory. These collaborations are instrumental to a possible technology infusion in future NASA missions.
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Marco Pavone |
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Brent W. Barbee |
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Julie C. Castillo-Rogez |
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Marco Quadrelli |
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Edward Schmerling |
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Joseph Starek |
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Lucas Janson |