Time-jerk optimal trajectory planning of a 7-DOF redundant robot

Abstract

In order to improve the efficiency and smoothness of a robot and reduce its vibration, an algorithm called the augmented Lagrange constrained particle swarm optimization (ALCPSO), which combines constrained particle swarm optimization with the augmented Lagrange multiplier method to realize time-jerk (defined as the derivative of the acceleration) optimal trajectory planning is proposed. Kinematic constraints such as joint velocities, accelerations, jerks, and traveling time are considered. The ALCPSO algorithm is used to avoid local optimization because a new particle swarm is newly produced at each initial time process. Additionally, the best value obtained from the former generation is saved and delivered to the next generation during the iterative search for process. Thus, the final best value can be found more easily and more quickly. Finally, the proposed algorithm is tested on the 7-DOF robot that is presented in this paper. The simulation results indicate that the algorithm is effective and feasible. Hence, the algorithm presents a solution for the time-jerk optimal trajectory planning problem of a robot subject to nonlinear constraints.