Path planning of mobile robot based on improved particle swarm optimization algorithm

An enhanced algorithm for optimizing the motion path of mobile robots in static environments is presented in this paper. The proposed algorithm, named as Improved Particle Swarm Optimization (IPSO), utilizes advanced techniques to enhance the traditional particle swarm optimization (PSO) approach, which addresses the issues of premature global optimization and unsatisfactory path accuracy when using traditional PSO. Firstly, consider the path length and collision risk as optimization objectives to improve the efficiency and safety of the robot's movement. Then, without compromising the fast convergence characteristics of the PSO algorithm, the inertia weight parameter of the algorithm are dynamically adjusted based on the current position of the particles, balancing the exploration of global or local regions in the environmental space. Finally, the proposed algorithm is applied to different experimental scenarios containing multiple static obstacles for path planning simulation experiments. The MATLAB simulation results show that compared with traditional PSO algorithm, the IPSO algorithm proposed in this paper has faster convergence velocity in complex environments, shorter planned path length, and stronger search ability. This study provides a practical and feasible solution for mobile robots to optimize their motion paths in static environments. It is expected to achieve wide application in robot path planning, improve the navigation and path planning ability of mobile robots, and promote the development of intelligent robot technology.