CLSQL: Improved Q-Learning Algorithm Based on Continuous Local Search Policy for Mobile Robot Path Planning

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 15; p. 5910
• Main Authors: Ma, Tian, Lyu, Jiahao, Yang, Jiayi, Xi, Runtao, Li, Yuancheng, An, Jinpeng, Li, Chao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 08.08.2022; MDPI
• Subjects: Artificial intelligence; complex environment; Control theory; Efficiency; Game theory; Genetic algorithms; Machine learning; mobile robot; Neural networks; Path planning; prior knowledge; Q-learning; Robots; Search process
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22155910

How to generate the path planning of mobile robots quickly is a problem in the field of robotics. The Q-learning(QL) algorithm has recently become increasingly used in the field of mobile robot path planning. However, its selection policy is blind in most cases in the early search process, which slows down the convergence of optimal solutions, especially in a complex environment. Therefore, in this paper, we propose a continuous local search Q-Learning (CLSQL) algorithm to solve these problems and ensure the quality of the planned path. First, the global environment is gradually divided into independent local environments. Then, the intermediate points are searched in each local environment with prior knowledge. After that, the search between each intermediate point is realized to reach the destination point. At last, by comparing other RL-based algorithms, the proposed method improves the convergence speed and computation time while ensuring the optimal path.