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A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot

Autonomous mobile robot path planning in unknown and dynamic environment is a crucial task for successful mobile robot navigation. This study proposes an improved Q-learning (IQL) algorithm to address the challenges of path planning in such environments. To this end, three different modes are intr...

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Main Authors: Low, Ee Soong, Ong, Pauline, Low, Cheng Yee
Format: Article
Language:English
Published: Elsevier 2023
Subjects:
TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
Online Access:http://eprints.uthm.edu.my/11646/1/J16153_c2bd76a73c1e817275f1aabce076fc0f.pdf
http://eprints.uthm.edu.my/11646/
https://doi.org/10.1016/j.cie.2023.109338
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spelling my.uthm.eprints.116462024-10-20T03:15:25Z http://eprints.uthm.edu.my/11646/ A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot Low, Ee Soong Ong, Pauline Low, Cheng Yee TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General) Autonomous mobile robot path planning in unknown and dynamic environment is a crucial task for successful mobile robot navigation. This study proposes an improved Q-learning (IQL) algorithm to address the challenges of path planning in such environments. To this end, three different modes are introduced into the IQL algorithm, namely the normal mode, the distortion mode, and the optimization mode. The normal mode operates according to the standard Q-learning procedures. The distortion mode distorts the Q-values of states around dynamic obstacles to facilitate avoidance, while the optimization mode is employed to overcome the local minimum problem. The efficacy of the IQL algorithm is assessed through a series of comparative studies involving fourteen navigation environments, each with distinct obstacle layouts and types. Comparative analyses are performed based on several metrics, including computational time, travelled distance, collision rate, and success rate. The proposed IQL algorithm exhibits a lower collision rate and a higher success rate when compared to dynamic window approach, influence zone and inflated Elsevier 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/11646/1/J16153_c2bd76a73c1e817275f1aabce076fc0f.pdf Low, Ee Soong and Ong, Pauline and Low, Cheng Yee (2023) A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot. Computers & Industrial Engineering, 181. pp. 1-29. https://doi.org/10.1016/j.cie.2023.109338
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
spellingShingle TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
Low, Ee Soong
Ong, Pauline
Low, Cheng Yee
A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot
description Autonomous mobile robot path planning in unknown and dynamic environment is a crucial task for successful mobile robot navigation. This study proposes an improved Q-learning (IQL) algorithm to address the challenges of path planning in such environments. To this end, three different modes are introduced into the IQL algorithm, namely the normal mode, the distortion mode, and the optimization mode. The normal mode operates according to the standard Q-learning procedures. The distortion mode distorts the Q-values of states around dynamic obstacles to facilitate avoidance, while the optimization mode is employed to overcome the local minimum problem. The efficacy of the IQL algorithm is assessed through a series of comparative studies involving fourteen navigation environments, each with distinct obstacle layouts and types. Comparative analyses are performed based on several metrics, including computational time, travelled distance, collision rate, and success rate. The proposed IQL algorithm exhibits a lower collision rate and a higher success rate when compared to dynamic window approach, influence zone and inflated
format Article
author Low, Ee Soong
Ong, Pauline
Low, Cheng Yee
author_facet Low, Ee Soong
Ong, Pauline
Low, Cheng Yee
author_sort Low, Ee Soong
title A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot
title_short A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot
title_full A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot
title_fullStr A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot
title_full_unstemmed A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot
title_sort modified q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot
publisher Elsevier
publishDate 2023
url http://eprints.uthm.edu.my/11646/1/J16153_c2bd76a73c1e817275f1aabce076fc0f.pdf
http://eprints.uthm.edu.my/11646/
https://doi.org/10.1016/j.cie.2023.109338
_version_ 1814055585520287744
score 13.211869

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