IMAGE-BASED OPTIMAL POWERED DESCENT GUIDANCE VIA DEEP RECURRENT IMITATION LEARNING

Luca Ghilardi, Andrea D’ambrosio, Andrea Scorsoglio, Roberto Furfaro, Richard Linares, Fabio Curti

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

Abstract

Future missions to the Moon and Mars will require autonomous landers/rovers to perform successful landing manoeuvres. In order to accomplish this task, reliable, fast and autonomous Guidance, Navigation, and Control (GNC) algorithms are necessary. In recent years, the strong capabilities of modern hardware have allowed employing deep learning models for space applications. In this paper, we present an image-based powered descent guidance via deep learning to control the command acceleration along the three axes. In particular, a hybrid architecture, composed of a Convolutional Neural Network and a Long Short Term Memory (CNN-LSTM), is trained using, as inputs, sequences of images taken during the descent. Hence, the neural network maps the sequences of images into the values of the command acceleration. The images are generated within a simulated environment with physically based ray-tracing capabilities.

Original languageEnglish (US)
Title of host publicationASTRODYNAMICS 2020
EditorsRoby S. Wilson, Jinjun Shan, Kathleen C. Howell, Felix R. Hoots
PublisherUnivelt Inc.
Pages2691-2706
Number of pages16
ISBN (Print)9780877036753
StatePublished - 2021
EventAAS/AIAA Astrodynamics Specialist Conference, 2020 - Virtual, Online
Duration: Aug 9 2020Aug 12 2020

Publication series

NameAdvances in the Astronautical Sciences
Volume175
ISSN (Print)0065-3438

Conference

ConferenceAAS/AIAA Astrodynamics Specialist Conference, 2020
CityVirtual, Online
Period8/9/208/12/20

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science

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