Time-optimal Trajectory Planning for Robotic Manipulator

To enhance the operational efficiency and stability of robotic manipulators, this study develops the kinematic model of the manipulator and employs a segmented "5-7-5" polynomial interpolation strategy for trajectory planning. An improved Particle Swarm Optimization (PSO) algorithm is proposed for the manipulator trajectory optimization where the Sobol sequences are incorporated for population initialization to ensure a more uniform distribution. The adaptive inertia weights are also used to dynamically adjust the learning factors to improve the convergence accuracy and global search capability. Under kinematic constraints, the trajectory of the manipulator is optimized with time-optimality as the objective via the improved PSO algorithm. The proposed method is validated on a UR5 robotic manipulator with benchmark tests and MATLAB simulations. Experimental results demonstrate that the robotic manipulator can operate with reduced execution time and enhanced operational efficiency, thereby confirming the feasibility and effectiveness of the proposed method.