Studying the Optimization of a Six-DOF Robotic Arm's Joint Angles Using an Enhanced Particle Swarm Technique

Abstract

In order to increase the precision of the end effector and the effectiveness of route planning, this work suggests a six-degree-of-freedom robotic arm joint angle path optimization technique based on an enhanced particle swarm optimization algorithm (PSO). By dynamically varying the inertia weight and acceleration factor, this article improves the method's global search capability and local search accuracy, therefore addressing the issue of premature convergence of the traditional PSO algorithm in high-dimensional situations. In addition, this paper designs an fitness function that targets the Euclidean distance between the end effector and the target position to guide the particle swarm to gradually approach the optimal solution. Simulation results show that the improved algorithm converges faster during the optimisation process, with a minimum error of 2.03324e-09, which verifies its effectiveness and practicality in complex path planning tasks. This method has broad application potential in fields such as industrial automation and medical robotics.