Path planning of unmanned vehicles based on adaptive particle swarm optimization algorithm

• Published in: Computer communications Vol. 216; pp. 112 - 129
• Main Authors: Zhao, Jiale, Deng, Chaoshuo, Yu, Huanhuan, Fei, Hansheng, Li, Deshun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.02.2024
• Subjects: Adaptive particle swarm optimization algorithm; dynamic obstacle avoidance strategy; Map simplification strategy; Path planning; Security checking strategy; Map simplification strategy; Path planning; Adaptive particle swarm optimization algorithm; dynamic obstacle avoidance strategy; Security checking strategy
• ISSN: 0140-3664; 1873-703X
• DOI: 10.1016/j.comcom.2023.12.040

Path planning technology is the basis of autonomous driving of unmanned vehicles. However, there are some problems in the traditional path planning technology. For example, high-quality global paths can't be generated quickly; Lacking of security verification ability; The performance of dynamic obstacle avoidance is poor. Therefore, this paper proposes a path planning method of unmanned vehicles based on adaptive particle swarm optimization algorithm (APSO). Firstly, a map simplification strategy (MSS) is proposed. The grid map is preprocessed by map simplification strategy to reduce the search space and time of path planning algorithm; Secondly, an APSO algorithm is proposed. The algorithm coordinates the search of particles through three adaptive factors and Levy flight strategy. Then, a security checking strategy is proposed. Security checking strategy can be used to verify the safety of global path; Finally, a dynamic obstacle avoidance strategy based on behavior is proposed. Vehicles can independently analyze the types of collision and adopt corresponding obstacle avoidance strategies. The simulation results show that MSS-APSO algorithm and APSO algorithm surpass original algorithms and comparison algorithms; MSS-APSO algorithm has strong applicability in real map environment; The obstacle avoidance strategy has great obstacle avoidance ability and real-time performance; The map simplification strategy can improve iterations of the algorithm and quality of the global path.