Improved Particle Swarm Optimization Algorithm for Mobile Robot Path Planning

Abstract

In solving the path planning problem of multi-path robots, an improved particle swarm optimization algorithm is proposed to address the drawbacks of premature convergence and low search accuracy of particle swarm optimization algorithm. Firstly, the improved Sine chaotic mapping is used to initialize the population, making it more evenly distributed in the search space and increasing population diversity. Then, the concept of quantum mechanics is introduced, which cancels the original particle movement speed and sets a new innovative parameter a instead. While reducing the parameters, the randomness of the particles is increased. Finally, the Levy flight strategy is used to improve the global search ability and convergence speed of the algorithm. The experimental results show that improving the particle swarm optimization algorithm for path planning enhances both local and global search capabilities. While minimizing algorithm complexity, it maximizes search accuracy and plans the shortest path that meets practical needs.