Path Planning of Mobile Robots Based on an Improved Particle Swarm Optimization Algorithm

Aiming at disadvantages of particle swarm optimization in the path planning of mobile robots, such as low convergence accuracy and easy maturity, this paper proposes an improved particle swarm optimization algorithm based on differential evolution. First, the concept of corporate governance is introduced, adding adaptive adjustment weights and acceleration coefficients to improve the traditional particle swarm optimization and increase the algorithm convergence speed. Then, in order to improve the performance of the differential evolution algorithm, the size of the mutation is controlled by adding adaptive parameters. Moreover, a high-intensity training mode is developed to use the improved differential evolution algorithm to intensively train the global optimal position of the particle swarm optimization, which can improve the search precision of the algorithm. Finally, the mathematical model for robot path planning is devised as a two-objective optimization with two indices, i.e., the path length and the degree of danger to optimize the path planning. The proposed algorithm is applied to different experiments for path planning simulation tests. The results demonstrate the feasibility and effectiveness of it in solving a mobile robot path-planning problem.

Automated summary

This paper proposes an improved particle swarm optimization algorithm based on differential evolution to address the disadvantages of low convergence accuracy and easy maturity in path planning of mobile robots. Adaptive adjustment weights and acceleration coefficients are added to enhance the convergence speed of the traditional particle swarm optimization algorithm. Additionally, adaptive parameters are introduced to control the mutation size and a high-intensity training mode is developed to improve the search precision of the algorithm. Experimental results demonstrate the feasibility and effectiveness of the proposed algorithm in solving mobile robot path-planning problems.