Solving the optimal path planning of a mobile robot using improved Q-learning

Low, Ee Soong and Ong, Pauline and Cheah, Kah Chun (2019) Solving the optimal path planning of a mobile robot using improved Q-learning. Robotics and Autonomous Systems, 115. pp. 143-161. ISSN 0921-8890

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Abstract

Q-learning, a type of reinforcement learning, has gained increasing popularity in autonomous mobile robot path planning recently, due to its self-learning ability without requiring a priori model of the environment. Yet, despite such advantage, Q-learning exhibits slow convergence to the optimal solution. In order to address this limitation, the concept of partially guided Q-learning is introduced wherein, the flower pollination algorithm (FPA) is utilized to improve the initialization of Q-learning. Experimental evaluation of the proposed improved Q-learning under the challenging environment with a different layout of obstacles shows that the convergence of Q-learning can be accelerated when Q-values are initialized appropriately using the FPA. Additionally, the effectiveness of the proposed algorithm is validated in a real-world experiment using a three-wheeled mobile robot.

Item Type:	Article
Uncontrolled Keywords:	Flower pollination algorithm; Obstacle avoidance; Path planning; Robot; Q-learning; Robot navigation
Subjects:	L Education > LB Theory and practice of education > LB1050.9-1091 Educational psychology T Technology > TJ Mechanical engineering and machinery > TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
Divisions:	Faculty of Mechanical and Manufacturing Engineering > Department of Mechanical Engineering
Depositing User:	Miss Afiqah Faiqah Mohd Hafiz
Date Deposited:	01 Dec 2021 05:41
Last Modified:	01 Dec 2021 05:41
URI:	http://eprints.uthm.edu.my/id/eprint/4217

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