Optimal time-jerk trajectory planning for industrial robots

A methodology for time-jerk synthetic optimal trajectory planning of robotic manipulators is described in this paper. The trajectory is interpolated in the joint space by means of 5th-order B-spline and then optimized by the elitist non-dominated sorting genetic algorithm (NSGA-II) for two objectives, namely, traveling time and mean jerk along the whole trajectory. 5th-order B-spline interpolation technique enables the trajectory to be constrained in the kinematic limits of velocity, acceleration, and jerk while satisfying the continuity of jerk. NSGA-II as a multi-objective optimization technique is used to address the time-jerk optimal trajectory planning problem. The obtained Pareto optimal front provides decision-makers flexible selections on non-dominated solutions for industrial applications. Two performance measures are presented to evaluate the strength of the Pareto optimal front and to select the best optimal solution respectively. Simulations and experiments validate the effectiveness and practicability of the proposed methodology in comparison with those provided by another important trajectory planning methodology.