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

Ee, Soong Low and Ong, Pauline and Kah, Chun Cheah (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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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: T Technology > T Technology (General)
Depositing User: Mr Abdul Rahim Mat Radzuan
Date Deposited: 29 Nov 2019 21:51
Last Modified: 29 Nov 2019 21:51
URI: http://eprints.uthm.edu.my/id/eprint/11976
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