An Improved Simulated Annealing Particle Swarm Optimization Algorithm for Path Planning of Mobile Robots Using Mutation Particles

Abstract

Artificial intelligence technology has brought tremendous changes to human life and production methods. Mobile robots, UAVs, and autonomous driving technology have gradually entered people’s daily life. As a typical issue for a mobile robot, the planning of an optimal mobile path is very important, especially in the military and emergency rescue. In order to ensure the efficiency of operation and the accuracy of the path, it is crucial for the robot to find the optimal path quickly and accurately. This paper discusses a new method and MP-SAPSO algorithm for addressing the issue of path planning based on the PSO algorithm by combining particle swarm optimization (PSO) algorithm with the simulated annealing (SA) algorithm and mutation particle and adjusting the parameters. The MP-SAPSO algorithm improves the accuracy of path planning and the efficiency of robot operation. The experiment also demonstrates that the MP-SAPSO algorithm can be used to effectively address path planning issue of mobile robots.

Comparative Analysis of Metaheuristic Optimization Methods for Trajectory Generation of Automated Guided Vehicles

“…Authors in [26] provided a comprehensive review of swarm intelligence algorithms, highlighting the wide application and discussion of extended algorithms. Additionally, recent studies have focused on improving PSO for path planning, such as the work by Lu et al in [27], which introduced an improved simulated annealing PSO algorithm for mobile robot path planning. Moreover, the research by Huang et al in [28] proposed a novel PSO algorithm based on reinforcement learning for autonomous underwater vehicle (AUV) path planning, addressing the consideration of ocean currents.…”