Heuristic Trajectory Planning of Robot Manipulator

Abstract

In this study, different Heuristic Algorithms (HAs) are used for optimizing the point-to-point trajectory planning for a robot manipulator. The objective function for the designed algorithms minimizes trajectory error, traveling time and space. In addition, the algorithms can obtain the optimum trajectory that connects the points avoiding impacts, as the robot manipulator moves from one position to another. Non-dominated sorting genetic algorithm-II (NSGA-II), genetic algorithm (GA), artificial bee colony (ABC) optimization and particle swarm optimization (PSO) are used to optimize the point-to-point motion planning for the robot arm motion studies in the trajectory planning. For each method, the optimal position trajectories towards a start point to target point have been shown. The best cost plots for all algorithms have been introduced. The results of the proposed trajectory planning optimization are compared by means of the travel distance and computation time. In order to compare the simulation results obtained by implementing the HAs, a robot manipulator has been used.