Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment

Jing Kai Lin; Shi Lin Ho; Kuan Yu Chou; Yon Ping Chen

doi:10.1109/ICCE-Taiwan55306.2022.9869215

Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment

Jing Kai Lin, Shi Lin Ho, Kuan Yu Chou^*, Yon Ping Chen

^*Corresponding author for this work

Institute of Electrical and Control Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Mobile robots with artificial intelligence are more and more popular on the rescue and human-service in complex environment. Path planning techniques for robots become the important topic to achieve it. Recently, Q-learning becomes a popular topic since the property of model-free. In this paper, generating the collision-free and optimal path with Q-learning for an mobile robot is proposed. Q-learning is adopted to let the mobile robot achieve the destination successfully through designing the states, actions and reward function in this paper. The system structure is integrated by two parts. First, the Q-learning algorithm is applied to find the collision-free and optimal path for an mobile robot. Second, Robot Operation System (ROS) is used to be the data transmission system among the dynamic path planning system, global position system and mobile robot. In the simulation result, the dynamic path planning system generates the collision-free and optimal path for the mobile robot. In addition, the movable obstacles appear on the original path suddenly, then the dynamic path planning system would regenerate a new optimal path to achieve the goal successfully.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	427-428
Number of pages	2
ISBN (Electronic)	9781665470506
DOIs	https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869215
State	Published - 2022
Event	2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 - Taipei, Taiwan Duration: 6 Jul 2022 → 8 Jul 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Conference

Conference	2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Country/Territory	Taiwan
City	Taipei
Period	6/07/22 → 8/07/22

Keywords

Q-learning
dynamic path planning
mobile robot
optimal and collision-free path planning
tracking control

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICCE-Taiwan55306.2022.9869215

Cite this

Lin, J. K., Ho, S. L., Chou, K. Y., & Chen, Y. P. (2022). Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment. In Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 (pp. 427-428). (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869215

Lin, Jing Kai ; Ho, Shi Lin ; Chou, Kuan Yu et al. / Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment. Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 427-428 (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022).

@inproceedings{e2485d68d0194595afcb75e7d691be23,

title = "Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment",

abstract = "Mobile robots with artificial intelligence are more and more popular on the rescue and human-service in complex environment. Path planning techniques for robots become the important topic to achieve it. Recently, Q-learning becomes a popular topic since the property of model-free. In this paper, generating the collision-free and optimal path with Q-learning for an mobile robot is proposed. Q-learning is adopted to let the mobile robot achieve the destination successfully through designing the states, actions and reward function in this paper. The system structure is integrated by two parts. First, the Q-learning algorithm is applied to find the collision-free and optimal path for an mobile robot. Second, Robot Operation System (ROS) is used to be the data transmission system among the dynamic path planning system, global position system and mobile robot. In the simulation result, the dynamic path planning system generates the collision-free and optimal path for the mobile robot. In addition, the movable obstacles appear on the original path suddenly, then the dynamic path planning system would regenerate a new optimal path to achieve the goal successfully.",

keywords = "Q-learning, dynamic path planning, mobile robot, optimal and collision-free path planning, tracking control",

author = "Lin, {Jing Kai} and Ho, {Shi Lin} and Chou, {Kuan Yu} and Chen, {Yon Ping}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 ; Conference date: 06-07-2022 Through 08-07-2022",

year = "2022",

doi = "10.1109/ICCE-Taiwan55306.2022.9869215",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022",

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Lin, JK, Ho, SL, Chou, KY & Chen, YP 2022, Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment. in Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022, Institute of Electrical and Electronics Engineers Inc., pp. 427-428, 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022, Taipei, Taiwan, 6/07/22. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869215

Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment. / Lin, Jing Kai; Ho, Shi Lin; Chou, Kuan Yu et al.
Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 427-428 (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment

AU - Lin, Jing Kai

AU - Ho, Shi Lin

AU - Chou, Kuan Yu

AU - Chen, Yon Ping

PY - 2022

Y1 - 2022

N2 - Mobile robots with artificial intelligence are more and more popular on the rescue and human-service in complex environment. Path planning techniques for robots become the important topic to achieve it. Recently, Q-learning becomes a popular topic since the property of model-free. In this paper, generating the collision-free and optimal path with Q-learning for an mobile robot is proposed. Q-learning is adopted to let the mobile robot achieve the destination successfully through designing the states, actions and reward function in this paper. The system structure is integrated by two parts. First, the Q-learning algorithm is applied to find the collision-free and optimal path for an mobile robot. Second, Robot Operation System (ROS) is used to be the data transmission system among the dynamic path planning system, global position system and mobile robot. In the simulation result, the dynamic path planning system generates the collision-free and optimal path for the mobile robot. In addition, the movable obstacles appear on the original path suddenly, then the dynamic path planning system would regenerate a new optimal path to achieve the goal successfully.

AB - Mobile robots with artificial intelligence are more and more popular on the rescue and human-service in complex environment. Path planning techniques for robots become the important topic to achieve it. Recently, Q-learning becomes a popular topic since the property of model-free. In this paper, generating the collision-free and optimal path with Q-learning for an mobile robot is proposed. Q-learning is adopted to let the mobile robot achieve the destination successfully through designing the states, actions and reward function in this paper. The system structure is integrated by two parts. First, the Q-learning algorithm is applied to find the collision-free and optimal path for an mobile robot. Second, Robot Operation System (ROS) is used to be the data transmission system among the dynamic path planning system, global position system and mobile robot. In the simulation result, the dynamic path planning system generates the collision-free and optimal path for the mobile robot. In addition, the movable obstacles appear on the original path suddenly, then the dynamic path planning system would regenerate a new optimal path to achieve the goal successfully.

KW - Q-learning

KW - dynamic path planning

KW - mobile robot

KW - optimal and collision-free path planning

KW - tracking control

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U2 - 10.1109/ICCE-Taiwan55306.2022.9869215

DO - 10.1109/ICCE-Taiwan55306.2022.9869215

M3 - Conference contribution

AN - SCOPUS:85138680073

T3 - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

SP - 427

EP - 428

BT - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Y2 - 6 July 2022 through 8 July 2022

ER -

Lin JK, Ho SL, Chou KY, Chen YP. Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment. In Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 427-428. (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022). doi: 10.1109/ICCE-Taiwan55306.2022.9869215

Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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