Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 19; p. 8260
• Main Authors: Sun, Ying, Wang, Wenlu, Xu, Manman, Huang, Li, Shi, Kangjing, Zou, Chunlong, Chen, Baojia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023; MDPI
• Subjects: Algorithms; Collaboration; dynamic path planning; dynamic windowing algorithm; efficient human–robot collaboration; Energy consumption; fuzzy control; Fuzzy logic; Kinematics; mobile robot; Planning; Real time; Robotics industry; Robots; Simulation; Velocity; China
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23198260

Due to the increased employment of robots in modern society, path planning methods based on human–robot collaborative mobile robots have been the subject of research in both academia and industry. The dynamic window approach used in the research of the robot local path planning problem involves a mixture of fixed weight coefficients, which makes it hard to deal with the changing dynamic environment and the issue of the sub-optimal global planning paths that arise after local obstacle avoidance. By dynamically modifying the combination of weight coefficients, we propose, in this research, the use of fuzzy control logic to optimize the evaluation function’s sub-functions and enhance the algorithm’s performance through the safe and dynamic avoidance of obstacles. The global path is introduced to enhance the dynamic window technique’s ability to plan globally, and important points on the global path are selected as key sub-target sites for the local motion planning phase of the dynamic window technique. The motion position changes after local obstacle avoidance to keep the mobile robot on the intended global path. According to the simulation results, the enhanced dynamic window algorithm cuts planning time and path length by 16% and 5%, respectively, while maintaining good obstacle avoidance and considering a better global path in the face of various dynamic environments. It is difficult to achieve a local optimum using this algorithm.