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
N1 - Publisher Copyright:
© 2022 IEEE.
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
UR - http://www.scopus.com/inward/record.url?scp=85138680073&partnerID=8YFLogxK
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 -
