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

Spacecraft motion planning 

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 and working papers

  • Bidirectional Fast Marching Trees: [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.

Marco Pavone 

Marco Pavone
Assistant Professor, PI
Stanford University
Department of Aeronautics and Astronautics

Brent W. Barbee 

Brent W. Barbee
Aerospace Engineer, NASA Technical Officer Representative
NASA Goddard Space Flight Center
Navigation and Mission Design Branch

Julie Castillo 

Julie C. Castillo-Rogez
Planetary Scientist, Collaborator
NASA Jet Propulsion Laboratory
Planetary Science

Marco Quadrelli 

Marco Quadrelli
Member of Technical Staff, Collaborator
NASA Jet Propulsion Laboratory
Robotic Controls and Estimation

Lucas Janson 

Lucas Janson
Ph.D. Student
Stanford University
Department of Statisics

Edward Schmerling 

Edward Schmerling
Ph.D. Student
Stanford University
Institute for Computational & Mathematical Engineering

Joseph Starek 

Joseph Starek
Ph.D. Student
Stanford University
Department of Aeronautics and Astronautics