Global Path Planning Method Based on Improved Particle Swarm Optimization Algorithm

Global path planning is a crucial research direction in intelligent navigation in robotics, unmanned aerial vehicles (UAVs), and autonomous driving. This paper proposes a global path planning method that integrates intelligent optimization algorithms. It aims to overcome the limitations of traditional methods. These include slow convergence and susceptibility to local optima in complex environments. The proposed method is based on the Cauchy Mutation Particle Swarm Optimization (CPSO) algorithm. It combines with the advantages of Genetic Algorithm (GA) and Ant Colony Optimization (ACO). The method designs a multi-objective path cost function to balance various factors. It considers path length, smoothness, and energy consumption. It enhances the ability to manage dynamic constraints. Experimental results show the proposed approach surpasses traditional methods. It excels in convergence speed and global optimization capability. This makes the approach practical for future applications.