Experimental research on deep reinforcement learning in autonomous navigation of mobile robot

Yue, P; Xin, J; Zhao, H; Liu, D; Shan, M; Zhang, J

Experimental research on deep reinforcement learning in autonomous navigation of mobile robot

Yue, P Xin, J Zhao, H Liu, D Shan, M Zhang, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019, 2019, pp. 1612-1616
Issue Date:: 2019-06-01

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Field	Value	Language
dc.contributor.author	Yue, P
dc.contributor.author	Xin, J
dc.contributor.author	Zhao, H
dc.contributor.author	Liu, D
dc.contributor.author	Shan, M
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-6102-3762
dc.date	2019-06-19
dc.date.accessioned	2020-06-17T19:16:15Z
dc.date.available	2020-06-17T19:16:15Z
dc.date.issued	2019-06-01
dc.identifier.citation	Proceedings of the 14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019, 2019, pp. 1612-1616
dc.identifier.isbn	9781538694909
dc.identifier.issn	2156-2318
dc.identifier.issn	2158-2297
dc.identifier.uri	http://hdl.handle.net/10453/141493
dc.description.abstract	© 2019 IEEE. The paper is concerned with the autonomous navigation of mobile robot from the current position to the desired position only using the current visual observation, without the environment map built beforehand. Under the framework of deep reinforcement learning, the Deep Q Network (DQN) is used to achieve the mapping from the original image to the optimal action of the mobile robot. Reinforcement learning requires a large number of training examples, which is difficult to directly be applied in a real robot navigation scenario. To solve the problem, the DQN is firstly trained in the Gazebo simulation environment, followed by the application of the well-trained DQN in the real mobile robot navigation scenario. Both simulation and real-world experiments have been conducted to validate the proposed approach. The experimental results of mobile robot autonomous navigation in the Gazebo simulation environment show that the trained DQN can approximate the state action value function of the mobile robot and perform accurate mapping from the current original image to the optimal action of the mobile robot. The experimental results in real indoor scenes demonstrate that the DQN trained in the simulated environment can work in the real indoor environment, and the mobile robot can also avoid obstacles and reach the target location even with dynamics and the presence of interference in the environment. It is therefore an effective and environmentally adaptable autonomous navigation method for mobile robots in an unknown environment.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the 14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019
dc.relation.ispartof	IEEE Conference on Industrial Electronics and Applications
dc.relation.isbasedon	10.1109/ICIEA.2019.8833968
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Experimental research on deep reinforcement learning in autonomous navigation of mobile robot
dc.type	Conference Proceeding
utslib.location.activity	Xi'an, China
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-17T19:16:04Z
pubs.finish-date	2019-06-21
pubs.place-of-publication	Psicataway, USA
pubs.publication-status	Published
pubs.start-date	2019-06-19
dc.location	Psicataway, USA

Abstract:

© 2019 IEEE. The paper is concerned with the autonomous navigation of mobile robot from the current position to the desired position only using the current visual observation, without the environment map built beforehand. Under the framework of deep reinforcement learning, the Deep Q Network (DQN) is used to achieve the mapping from the original image to the optimal action of the mobile robot. Reinforcement learning requires a large number of training examples, which is difficult to directly be applied in a real robot navigation scenario. To solve the problem, the DQN is firstly trained in the Gazebo simulation environment, followed by the application of the well-trained DQN in the real mobile robot navigation scenario. Both simulation and real-world experiments have been conducted to validate the proposed approach. The experimental results of mobile robot autonomous navigation in the Gazebo simulation environment show that the trained DQN can approximate the state action value function of the mobile robot and perform accurate mapping from the current original image to the optimal action of the mobile robot. The experimental results in real indoor scenes demonstrate that the DQN trained in the simulated environment can work in the real indoor environment, and the mobile robot can also avoid obstacles and reach the target location even with dynamics and the presence of interference in the environment. It is therefore an effective and environmentally adaptable autonomous navigation method for mobile robots in an unknown environment.

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http://hdl.handle.net/10453/141493