Trajectory Generation of an Industrial Robot With Constrained Kinematic and Dynamic Variations for Improving Positional Accuracy

Abstract

The joint trajectory of the robot needs to be computed in an optimal manner for proper torch orientation, smooth travel of the robot along the trajectory path. This can be achieved by limiting the travel time, kinematic and dynamic variations of the robot joints like the jerks, and torque induced in the joints in the travel of the robot. As the objectives of total travel time and joint jerk and torque rate are contradictory functions, non-dominated sorting genetic algorithm-II (NSGA-II) approach has been used to obtain the pareto front consisting of optimal solutions. The fuzzy membership function has been used to obtain the optimal solution from the pareto front with best trade-off between objectives for further optimal trajectory generation. From the simulation results, it can be concluded that the proposed approach can be effectively used for optimal trajectory planning of Kawasaki RS06L industrial manipulator with minimal jerk, torque rate, and total travel time for smooth travel of robot with higher positional accuracy.