Time-Jerk Optimal Trajectory Planning of Industrial Robot based on Hybrid Particle Swarm Optimization Algorithm

Abstract

Industrial robots need to execute specific trajectories on production lines, which requires rational planning of robot joint trajectories in Cartesian space. To improve the efficiency and control accuracy of industrial robots, time- jerk multi-objective trajectory optimization is required. The main research of this topic is to apply the particle swarm optimization(PSO) algorithm based on crossover and subgroup to complete the trajectory optimization of a six-degree-of- freedom robot in Cartesian space with time-jerk multi-objective function and compare the optimization effect of the hybrid particle swarm optimization algorithm relative to the traditional particle swarm optimization algorithm. Firstly, a seven segment S-Spline interpolation function is applied to complete the trajectory planning and the basic PSO algorithm is applied to optimize the trajectory for the interpolation time. Then based on the shortcomings of the basic PSO algorithm, the particle swarm optimization algorithm based on crossover and subgroup is proposed to improve the speed and effectiveness of the trajectory optimization algorithm. Finally, simulation experiments show that the total time and joint shocks of robot trajectory planning are greatly reduced, which verifies the effectiveness of the hybrid PSO algorithm applied to robot time-shock optimal trajectory optimization.