Year: 2023
Type: conference-paper
Abstract: The Q-Learning (QL) algorithm is widely used for path planning. As the scene in which the mobile robot is located becomes complex, the algorithm suffers from the limitations of low convergence speed and long exploration paths. Therefore, a Max Reward-Q-learning (MR-QL) path planning algorithm based on maximum reward is proposed for complex unknown scenarios. The original algorithm’s discrete reward function and action selection strategy are improved, and a new reward function is designed to dynamically adjust the reward mechanism to heuristically guide the robot motion. The action selection strategy is also optimized to avoid invalid exploration and improve the algorithm’s convergence. Finally, three experimental environments with different complexity are constructed to demonstrate the feasibility of the proposed algorithm. The simulation results show that the MR-QL algorithm is about 50% of the original algorithm in terms of exploration step length and training time, and the convergence speed of the algorithm is better than the original algorithm.
Authors: Zongli Fu, Zhandong Liu, Chuangye Hu, Nan Ding
Institutions: Xinjiang Normal University, Dalian University of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4378904830