期刊
ROBOTICS AND AUTONOMOUS SYSTEMS
卷 115, 期 -, 页码 143-161出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.robot.2019.02.013
关键词
Flower pollination algorithm; Obstacle avoidance; Path planning; Robot; Q-learning; Robot navigation
资金
- Ministry of Education Malaysia through the Fundamental Research Grant Scheme, Malaysia [FRGS-Vot K070]
- Universiti Tun Hussein Onn Malaysia (UTHM) [Vot H034]
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. (C) 2019 Elsevier B.V. All rights reserved.
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