Time-Optimal Trajectory Planning for Industrial Robot based on Improved Hybrid-PSO

Abstract

To improve the actual performance of the time-optimal trajectory planning of industrial robots, a trajectory planning method based on the improved hybrid particle swarm optimization algorithm (Hybrid-PSO) is proposed in this paper. The strategy is designed as a combination of the planning with NURBS in Cartesian space and cubic B-spline in joint space. Following implementation, on the premise of accurately passing the required data points, NURBS is used for Cartesian space planning, while cubic B-spline is used in joint space to plan the trajectory through the data points (obtained from Cartesian space planning) of joint angle. The method of separately planning the trajectory of each joint angle is used to avoid the disaster of the high dimension. A bionic rollback algorithm inspired by the behavior of natural bird blocks is proposed, which reduces the possibility of the particle swarm optimization processes entering the local optimal state, and at the same time increases the diversity of the population. The simulation results show that this method is an effective trajectory planning method, which can generate a smooth, time-optimal trajectory for industrial robots.