Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach

Roberto Furfaro, Andrea Scorsoglio, Richard Linares, Mauro Massari

Research output: Contribution to journalArticle

Abstract

Precision landing on large and small planetary bodies is a technology of utmost importance for future human and robotic exploration of the solar system. In this context, the Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) feedback guidance algorithm has been studied extensively and is still a field of active research. The algorithm, although powerful in terms of accuracy and ease of implementation, has some limitations. Therefore with this paper we present an adaptive guidance algorithm based on classical ZEM/ZEV in which machine learning is used to overcome its limitations and create a closed loop guidance algorithm that is sufficiently lightweight to be implemented on board spacecraft and flexible enough to be able to adapt to the given constraint scenario. The adopted methodology is an actor-critic reinforcement learning algorithm that learns the parameters of the above-mentioned guidance architecture according to the given problem constraints.

Original languageEnglish (US)
Pages (from-to)156-171
Number of pages16
JournalActa Astronautica
Volume171
DOIs
StatePublished - Jun 2020

Keywords

  • Closed-loop guidance
  • Deep reinfocement learning
  • Optimal landing guidance

ASJC Scopus subject areas

  • Aerospace Engineering

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