Application of Actor-Critic Deep Reinforcement Learning Method for Obstacle Avoidance of WMR

Xiaoshan Gao, Liang Yan, Gang Wang, Zhuang He, Chris Gerada, Suokui Chang

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

Abstract

A state-of-the-art framework, i.e., deep deterministic policy gradient (DDPG), has obtained a certain effect in the robotic control field. When the wheeled mobile robot (WMR) executes operation in unstructured environment, it is critical to endow the WMR with the capacity to avoid the static and dynamic obstacles. Thus, a obstacle avoidance algorithm based on DDPG is proposed to realize the autonomous navigation in the unknown environment. The WMR in this study installs the requisite sensors to provide the fully observable environment information at any moment. The continuous state space description for WMR and obstacles is designed, together with the reward mechanism and action space. The learning agent. i.e., the studied mobile robot, utilizes the DDPG model, through the continuous interaction with the surrounding environment and the application of historical experience data, the WMR can learn the optimal action behavior. Simulation along with test works strongly verify the collision-free ability in static and dynamic scenarios with multiple observable obstacles.

Original languageEnglish
Title of host publicationAdvances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020
EditorsLiang Yan, Haibin Duan, Xiang Yu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages5485-5494
Number of pages10
ISBN (Print)9789811581540
DOIs
Publication statusPublished - 2022
Externally publishedYes
EventInternational Conference on Guidance, Navigation and Control, ICGNC 2020 - Tianjin, China
Duration: 23 Oct 202025 Oct 2020

Publication series

NameLecture Notes in Electrical Engineering
Volume644 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Guidance, Navigation and Control, ICGNC 2020
Country/TerritoryChina
CityTianjin
Period23/10/2025/10/20

Keywords

  • Deep reinforcement learning
  • Obstacle avoidance
  • Wheeled mobile robot

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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