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Stochastic Motion Planning for Hopping Rovers on Small Solar System Bodies

  • Benjamin HockmanEmail author
  • Marco Pavone
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 10)

Abstract

Hopping rovers have emerged as a promising platform for the future surface exploration of small Solar System bodies, such as asteroids and comets. However, hopping dynamics are governed by nonlinear gravity fields and stochastic bouncing on highly irregular surfaces, which pose several challenges for traditional motion planning methods. This paper presents the first ever discussion of motion planning for hopping rovers that explicitly accounts for various sources of uncertainty. We first address the problem of planning a single hopping trajectory by developing (1) an algorithm for robustly solving Lambert’s orbital boundary value problems in irregular gravity fields, and (2) a method for computing landing distributions by propagating control and model uncertainties—from which, a time/energy-optimal hop can be selected using a (myopic) policy gradient. We then cast the sequential planning problem as a Markov decision process and apply a sample-efficient, off-line, off-policy reinforcement learning algorithm—namely, a variant of least squares policy iteration (LSPI)—to derive approximately optimal control policies that are safe, efficient, and amenable to real-time implementation on computationally-constrained rover hardware. These policies are demonstrated in simulation to be robust to modelling errors and outperform previous heuristics.

Keywords

Space robotics Planning under uncertainty Reinforcement learning 

Notes

Acknowledgements

This work is supported by NASA under the Innovative Advanced Concepts program.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Robotic Mobility GroupJet Propulsion Laboratory, California Institute of TechnologyPasadenaUSA
  2. 2.Department of Aeronautics and AstronauticsStanford UniversityStanfordUSA

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