Optimal time-jerk-torque trajectory planning of industrial robot under kinematic and dynamic constraints

In this paper, an offline optimal trajectory planning of 6-axis industrial subjected to both kinematic and dynamic constraints has been presented. The positional accuracy of the robot end effector and smoothness of the robot travel can be achieved by minimizing the torque rate and joint jerks. But this results in vast increase in total travel time of robot which as a result affects the productivity. This leads to a formulation of a multi-objective optimization, as jerk and torque rate functions are contradictory to time interval function. Therefore, a new and efficient Hybrid Multi-Objective Evolutionary Algorithm (HMOEA) i.e. Non-Dominated Sorting Genetic Algorithm (NSGA-II) combined with Nelder-Mead simplex method with better local search has been proposed to obtain an optimal Pareto front consisting of non-dominated solutions that can give best trade-off between the objectives. Finally, the optimal results have been validated through simulation and experiment using Kawasaki RS06L 6-axis robot available in product development laboratory of department.