Application of an Improved Particle Swarm Optimization Algorithm in the Robotic Arm of a Handling Robot - Zhang Zhenhe; Yang Daoyu; Shu Yibin; Liu Jiangyi; Cao Jingyi; Chen Meirong (Jixie chuandong)
Aiming at the time optimization problem of the space planning of the handling robot, an improved particle swarm optimization (PSO) with dynamic learning factor, variable inertia weight factor and beetle antennae search (BAS) algorithm is proposed. The workspace is obtained by the kinematics analysis. The 3-5-3 polynomial interpolation is introduced for trajectory planning. The acceleration and velocity of the moving process are restrained, and the shortest time of the moving process is obtained. The convergence speed of the improved PSO is compared, and the movement time of each joint is analyzed and then verified by simulation and experiment. The learning factor is set as a variable to make the algorithm jump out of local optimum. The variable inertia weight factor improves the search efficiency. Combined with the BAS algorithm, the speed and precision of the search algorithm are improved. The results show that the convergence speed and accuracy of the improved PSO algorithm are improved, the local optimality is avoided, and the overall motion time is reduced by about 15.9%. The joint angle, velocity and acceleration curves of the robotic arm are smooth and stable, and the improved algorithm is effective.