Robust spacecraft hovering near small bodies in environments with unknown dynamics using reinforcement learning

Brian Gaudet, Roberto Furfaro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

Autonomous close proximity operations (including hovering and landing) in the lowgravity environment exhibited by asteroids are particularly challenging. Current approaches to this problem require knowledge of the environmental dynamics in the asteroid's vicinity. This knowledge is costly, both in terms of time and money, to acquire. This paper uses reinforcement learning (RL) to develop a novel non-linear hovering controller with sufficient robustness to allow precision hovering in unknown environments, limited only by the maximum thrust requirements imposed by the environment. We demonstrate the robustness of the controller by simulating precision hovering in multiple environments that were unknown during the policy optimization. The environments are modeled using non-uniform rotation and a non-uniform gravity field. Simulations were also run using a shape model of the asteroid Itokawa. Performance is compared to that of an RL derived optimal linear PD controller and an LQR controller. Since the hovering controller requires an estimate of the spacecraft's state relative to a landmark on the asteroid's surface, we also introduce an optical seeker based navigation approach that accurately estimates the spacecraft's current state using only a single camera and laser range finder.

Original languageEnglish (US)
Title of host publicationAIAA/AAS Astrodynamics Specialist Conference 2012
DOIs
StatePublished - Dec 1 2012
EventAIAA/AAS Astrodynamics Specialist Conference 2012 - Minneapolis, MN, United States
Duration: Aug 13 2012Aug 16 2012

Publication series

NameAIAA/AAS Astrodynamics Specialist Conference 2012

Other

OtherAIAA/AAS Astrodynamics Specialist Conference 2012
CountryUnited States
CityMinneapolis, MN
Period8/13/128/16/12

ASJC Scopus subject areas

  • Aerospace Engineering
  • Astronomy and Astrophysics

Fingerprint Dive into the research topics of 'Robust spacecraft hovering near small bodies in environments with unknown dynamics using reinforcement learning'. Together they form a unique fingerprint.

  • Cite this

    Gaudet, B., & Furfaro, R. (2012). Robust spacecraft hovering near small bodies in environments with unknown dynamics using reinforcement learning. In AIAA/AAS Astrodynamics Specialist Conference 2012 (AIAA/AAS Astrodynamics Specialist Conference 2012). https://doi.org/10.2514/6.2012-5072