Mobile robot path planning using q-learning with guided Distance

• Main Authors: Ee, Soong Low, Pauline, Ong, Cheng, Yee Low
• Format: Article
• Language: English
• Published: Science Publishing Corporation 2018
• Subjects: TJ1040-1119 Machinery exclusive of prime movers; TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
• Online Access: http://eprints.uthm.edu.my/3680/1/AJ%202018%20%28721%29%20Mobile%20robot%20path%20planning%20using%20q-learning%20with%20guided.pdf; http://eprints.uthm.edu.my/3680/

In path planning for mobile robot, classical Q-learning algorithm requires high iteration counts and longer time taken to achieve conver-gence. This is due to the beginning stage of classical Q-learning for path planning consists of mostly exploration, involving random di-rection decision making. This paper proposed the addition of distance aspect into direction decision making in Q-learning. This feature is used to reduce the time taken for the Q-learning to fully converge. In the meanwhile, random direction decision making is added and activated when mobile robot gets trapped in local optima. This strategy enables the mobile robot to escape from local optimal trap. The results show that the time taken for the improved Q-learning with distance guiding to converge is longer than the classical Q-learning. However, the total number of steps used is lower than the classical Q-learning.