Path Planning Based on Improved Particle Swarm Optimization Algorithm

The traditional particle swarm optimization (PSO) algorithm has some shortcomings such as low convergence precision, stagnant search and so on, which lead to the low precision of robot path planning. In order to improve the precision of path planning, the traditional particle swarm optimization algorithm was improved. Firstly, the inertia weight factor and acceleration factor were adjusted adaptively by the trigonometric function in each stage of the algorithm operation, so that the parameters in the algorithm were optimized in each stage of the algorithm operation, and the search ability of the algorithm was improved. Secondly, the hen equation and chick equation of chicken swarm algorithm were introduced to perturb the search stagnation particles, and the global optimal solution was used in the introduced equation to make the disturbed particle approach the global optimal solution. Finally, through two sets of comparative experiments of function optimization and path planning, it was proved that the improved algorithm had the advantages of high searching precision and good robustness. © 2018, Chinese Society of Agricultural Machinery. All right reserved.