Deep learning for autonomous lunar landing

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

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

27 Scopus citations


Over the past few years, encouraged by advancements in parallel computing technologies (e.g., Graphic Processing Units, GPUs), availability of massive labeled data as well as breakthrough in understanding of deep neural networks, there has been an explosion of machine learning algorithms that can accurately process images for classification and regression tasks. It is expected that deep learning methods will play a critical role in autonomous and intelligent space guidance problems. The goal of this paper is to design a set of deep neural networks, i.e. Convolutional Neural Networks (CNN) and Recurrent Neural Net-works (RNN) which are able to predict the fuel-optimal control actions to perform autonomous Moon landing, using only raw images taken by on board optimal cameras. Such approach can be employed to directly select actions with-out the need of direct filters for state estimation. Indeed, the optimal guidance is determined processing the images only. For this purpose, Supervised Machine Learning algorithms are designed and tested. In this framework, deep networks are trained with many example inputs and their desired outputs (labels), given by a supervisor. During the training phase, the goal is to model the unknown functional relationship that links the given inputs with the given outputs. Inputs and labels come from a properly generated dataset. The images associated to each state are the inputs and the fuel-optimal control actions are the labels. Here we consider two possible scenarios, i.e. 1) a vertical 1-D Moon landing and 2) a planar 2-D Moon landing. For both cases, fuel-optimal trajectories are generated by software packages such as the General Pseudospectral Optimal Control Software (GPOPS) considering a set of initial conditions. With this dataset a training phase is performed. Subsequently, in order to improve the network accuracy a Dataset Aggregation (Dagger) approach is applied. Performances are verified on test optimal trajectories never seen by the networks.

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 pages22
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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