An improved fuzzy‐controlled local path planning algorithm based on dynamic window approach

• Published in: Journal of field robotics Vol. 42; no. 2; pp. 430 - 454
• Main Authors: Liu, Aizun, Liu, Chong, Li, Lei, Wang, Ruchao, Lu, Zhiguo
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.03.2025
• Subjects: Algorithms; Barriers; Controllers; DWA algorithm; dynamic obstacle avoidance; Fuzzy control; local optimal; local path planning; mobile robot; Navigation; Path planning; Robot dynamics; Robots; Similarity; Trajectories; trajectory similarity
• ISSN: 1556-4959; 1556-4967
• DOI: 10.1002/rob.22419

With the increasingly complex operating environment of mobile robots, the intelligent requirements of robots are getting higher and higher. Navigation technology is the core of mobile robot intelligent technology research, and path planning is an important function of mobile robot navigation. Dynamic window approach (DWA) is one of the most popular local path planning algorithms nowadays. However, there are also some problems. DWA algorithm is easy to fall into local optimal solution without the guidance of global path. The traditional solution is to use the key nodes of the global path as the temporary target points. However, the guiding ability of the temporary target points will be weakened in some cases, which still leads DWA to fall into local optimal solutions such as being trapped by a “C”‐shaped obstacle or go around outside of a dense obstacle area. In a complex operating environment, if the local path deviates too far from the global path, serious consequences may be caused. Therefore, we proposed a trajectory similarity evaluation function based on dynamic time warping method to provide better guidance. The other problem is poor adaptability to complex environments due to fixed evaluation function weights. And, we designed a fuzzy controller to improve the adaptability of the DWA algorithm in complex environments. Experiment results show that the trajectory similarity evaluation function reduces algorithm execution time by 0.7% and mileage by 2.1%, the fuzzy controller reduces algorithm execution time by 10.8% and improves the average distance between the mobile robot and obstacles at the global path's danger points by 50%, and in simulated complex terrain environment, the finishing rate of experiments improves by 25%.