Optimal time-jerk trajectory planning for industrial robots

• Published in: Mechanism and machine theory Vol. 121; pp. 530 - 544
• Main Authors: Huang, Junsen, Hu, Pengfei, Wu, Kaiyuan, Zeng, Min
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2018
• Subjects: 5th-order B-spline; Multi-objective optimization; NSGA-II; Robotic manipulator; Time-jerk optimal trajectory; NSGA-II; Multi-objective optimization; Robotic manipulator; Time-jerk optimal trajectory; 5th-order B-spline
• ISSN: 0094-114X; 1873-3999
• DOI: 10.1016/j.mechmachtheory.2017.11.006

•A methodology for obtaining optimal time-jerk trajectory of robot manipulator is proposed. The methodology applies 5th-order B-spline interpolation method to construct the trajectory and optimizes the trajectory with NSGA-II algorithm.•Two virtual points are introduced in the process of B-spline modeling so that the initial and final conditions for jerk can be respected.•A number of solutions lying on or near the Pareto-optimal front are obtained using NSGA-II algorithm.•Improved performance measures are proposed to evaluate the diversity of the Pareto front and the fitness of the Pareto solutions.•Simulated and experimental results validate the proposed methodology together. 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.