Self-learning exploration and mapping for mobile robots via deep reinforcement learning

Fanfei Chen, Shi Bai, Tixiao Shan, Brendan Englot

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

32 Scopus citations

Abstract

Mapping and exploration of a priori unknown environments is a crucial capability for mobile robot autonomy. A state-of-the-art approach for mobile robots equipped with range sensors uses mutual information as the basis for a cost metric [5], [14], and reasons about how much information gain is associated with each action a robot can take while constructing an occupancy map from its range measurements. However, the computational cost of such an optimization scales poorly as the number of potential robot actions increases. We propose a novel approach to utilize the local structure of the environment while predicting a robot’s optimal sensing action using Deep Reinforcement Learning (DRL) [19]. The learned exploration policy can select an optimal or near-optimal exploratory sensing action with improved computational efficiency. Our computational results demonstrate that the proposed method provides both efficiency and accuracy in choosing informative sensing actions.

Original languageEnglish
Title of host publicationAIAA Scitech 2019 Forum
DOIs
StatePublished - 2019
EventAIAA Scitech Forum, 2019 - San Diego, United States
Duration: 7 Jan 201911 Jan 2019

Publication series

NameAIAA Scitech 2019 Forum

Conference

ConferenceAIAA Scitech Forum, 2019
Country/TerritoryUnited States
CitySan Diego
Period7/01/1911/01/19

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