Path Planning for Manipulator Based on Optimized Particle Swarm Algorithm

To solve the problems of premature convergence and easy to fall into local optimality search of traditional particle swarm algorithm (PSO) in robotic arm path planning. In this paper, Gaussian perturbation and natural selection strategy are introduced, and an adaptive inertia weight is constructed to improve the convergence speed and global search ability of the particle swarm algorithm as well as the ability to jump out of the local optimum, and also to make it more adaptable to the complex working environment. The effectiveness of the algorithm is verified by using MATLAB to establish a simulation model of the robotic arm. Under the same experimental conditions, compared with the traditional particle swarm algorithm, the optimized algorithm achieves significant improvement in convergence speed, convergence accuracy and global search ability, which provides strong support for the research and practical application in the field of robotic arm path planning.