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

Abstract

Industrial robot has been widely used since the development of manufacturing. A time-optimal trajectory planning for industrial robot can help to save energy and improve efficiency. This paper proposed a new trajectory planning method based on improved particle swarm optimization. Golden section method is used to find the maximum of velocity, acceleration and jerk. Compared to derivative method, way used Golden section method can find the maximum more quickly. Roll-back technology is introduced to ensure the diversity of particle swarm, which makes sure that the search ability will not be weaken by the reduction of number of particles. An improved particle swarm optimization with variable learning factor is used to search the global best solution. The results of simulation show that the proposed trajectory planning method can generate a time-optimal trajectory for industrial robot.