Integrated guidance and control for pinpoint mars landing using reinforcement learning

Brian Gaudet, Richard Linares, Roberto Furfaro

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

6 Scopus citations


Future Mars missions will require advanced guidance, navigation, and control algorithms for the powered descent phase in order to target specific surface locations and achieve pinpoint accuracy (landing error ellipse < 5m radius). This requires both a navigation system capable of estimating the lander’s state in real-time and a guidance and control system that can map the estimated lander state to body-frame actuator commands. In this paper we present a novel integrated guidance and control algorithm designed by applying the principles of reinforcement learning theory. The key innovation is the use of reinforcement learning to learn a policy mapping the lander’s estimated state directly to actuator commands, with the policy resulting in accurate and fuel efficient trajectories. Specifically, we use proximal policy optimization, a policy gradient method, to learn the policy. We present simulation results demonstrating the guidance and control system’s performance in a 6-DOF simulation environment, and demonstrate robustness to noise and system parameter uncertainty.

Original languageEnglish (US)
Title of host publicationAAS/AIAA Astrodynamics Specialist Conference, 2018
EditorsPuneet Singla, Ryan M. Weisman, Belinda G. Marchand, Brandon A. Jones
PublisherUnivelt Inc.
Number of pages20
ISBN (Print)9780877036579
StatePublished - 2018
EventAAS/AIAA Astrodynamics Specialist Conference, 2018 - Snowbird, United States
Duration: Aug 19 2018Aug 23 2018

Publication series

NameAdvances in the Astronautical Sciences
ISSN (Print)0065-3438


ConferenceAAS/AIAA Astrodynamics Specialist Conference, 2018
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science


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