A recurrent deep architecture for quasi-optimal feedback guidance in planetary landing

Roberto Furfaro, Ilaria Bloise, Marcello Orlandelli, Pierluigi Di Lizia, Francesco Topputo, Richard Linares

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

Abstract

Precision landing on large planetary bodies is an important technology that enables future human and robotic exploration of the solar system. For example, over the past decade, landing systems for robotic missions have been developed with the specific goal of deploying robotic agents (e.g. rovers, landers) on the planetary surface (e.g. Mars, Moon). Considering the strong interest for sending humans back to the Moon within the next decade, the landing system technology will continue to progress to keep up with the demand for more stringent requirements. Indeed, more demanding planetary exploration requirements implies a technology development program that calls for more precise guidance systems capable of delivering rovers and/or landers with higher and higher degree of precision. In this paper we design, test and validate a deep Recurrent Neural Network (RNN) architecture capable of predicting the fuel-optimal thrust from sequence of states during a powered planetary descent. Here, the principle behind imitation learning (super-vised learning) are applied. A set of propellant-optimal open loop landing trajectories are computed using direct transcription methods (e.g. Gauss Pseudo Spectral methods). Such sequences comprise the training set (i.e. the teacher) employed during the learning phase. A Long-Short Term Memory (LSTM) architecture is employed to keep track of what has entered the network before and use such information to better predict the output. The RNN-LSTM architecture is trained validated and tested to evaluate the performance predictive performance. Finally, the results of a Monte Carlo simulations in Moon landing scenarios are provided to show the effectiveness of the proposed methodology.

Original languageEnglish (US)
Title of host publication1st IAA/AAS SciTech Forum on Space Flight Mechanics and Space Structures and Materials, 2018
EditorsYury N. Razoumny, Filippo Graziani, Anna D. Guerman, Jean-Michel Contant
PublisherUnivelt Inc.
Pages151-174
Number of pages24
ISBN (Print)9780877036630
StatePublished - Jan 1 2020
Event1st IAA/AAS SciTech Forum on Space Flight Mechanics and Space Structures and Materials, 2018 - Moscow, Russian Federation
Duration: Nov 13 2018Nov 15 2018

Publication series

NameAdvances in the Astronautical Sciences
Volume170
ISSN (Print)0065-3438

Conference

Conference1st IAA/AAS SciTech Forum on Space Flight Mechanics and Space Structures and Materials, 2018
CountryRussian Federation
CityMoscow
Period11/13/1811/15/18

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ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science

Cite this

Furfaro, R., Bloise, I., Orlandelli, M., Di Lizia, P., Topputo, F., & Linares, R. (2020). A recurrent deep architecture for quasi-optimal feedback guidance in planetary landing. In Y. N. Razoumny, F. Graziani, A. D. Guerman, & J-M. Contant (Eds.), 1st IAA/AAS SciTech Forum on Space Flight Mechanics and Space Structures and Materials, 2018 (pp. 151-174). (Advances in the Astronautical Sciences; Vol. 170). Univelt Inc..