Dynamic Path Planning for Cooperative Robots Based on the Hybrid SA-PSO Algorithm

Abstract

Addressing the issues of particle swarm optimization (PSO) in robot path planning, such as getting trapped in local optima, improper parameter settings, and low computational efficiency, this paper proposes a novel hybrid optimization strategy that combines PSO with simulated annealing (SA). This hybrid algorithm integrates the advantages of both algorithms, leveraging the rapid convergence ability of PSO and the global search capability of SA to provide a more efficient, flexible, and robust solution. It is particularly suitable for complex, dynamic, and multi-objective scenarios and can be used to solve the path planning problem of cooperative robots in complex and changing environments. Through simulation experiments, this article has verified the effectiveness of the algorithm. The results show that, compared to traditional PSO and SA algorithms, this algorithm demonstrates higher performance and efficiency in planning the trajectory of a robot in a complex dynamic environment.