A General Robot Inverse Kinematics Solution Method Based on Improved PSO Algorithm

Abstract

Robots whose geometric structure does not meet the Pieper criterion are called general robots. For the inverse kinematic operation of general robots, the closed solution method cannot be solved, and the numerical solution calculation amount is too large and the singular position cannot be calculated. To solve this problem, this paper proposes an inverse kinematics calculation method based on improved particle swarm optimization (PSO) algorithm and applicable to general robots. In order to avoid the particle update rate not adapting to each stage of the optimization process, a nonlinear dynamic inertia weight adjustment method based on the concept of similarity is introduced, so that the search process is more robust; in addition, to overcome the problem of local optimal solution At the same time, multiple populations are introduced to perform optimization search at the same time, and the immigration operator is proposed to increase the diversity of the particle population in the iteration. This article uses Comau NJ-220 robot for test verification, compared with the original PSO and multi-subswarm algorithm, the results show that the proposed improved PSO has higher algorithm stability for general robot kinematics inverse solution problems, and can greatly improve the convergence accuracy and speed. This method provides a new solution to the field of robot inverse kinematics, and provides a more efficient and stable kinematics foundation for robot motion planning.

Keywords

• Robot kinematics analysis
• PSO algorithm
• robot inverse kinematics
• artificial intelligence