Comparative Study of Different Metaheuristics for the Trajectory Planning of a Robotic Arm

Abstract

In this paper, seven different metaheuristic optimization algorithms developed between 2005 and 2012 are applied to optimize the robot trajectory for a three-revolute (3R) robotic arm. These algorithms include the artificial bee colony (ABC) algorithm, biogeography-based optimization, gravitational search algorithm, cuckoo search algorithm (CS), firefly algorithm, bat algorithm, and teaching–learning-based optimization (TLBO). This work presents the optimization problem with the objective to plan a trajectory, which can minimize joint travelling time, joint travelling distance, and total joint Cartesian lengths, simultaneously. Nine different design variables are considered for the intermediate joint angles, joint velocities, the end-effector angle for the final configuration, the time from initial to intermediate configuration, and the time from intermediate to final configuration. Two different experiments are conducted to investigate the effect of different considered metaheuristics: 1) for the free workspace and 2) for the workspace with obstacle. All these algorithms are compared based on the algorithm ability to find the best solutions, mean solutions, standard deviation, computational effort, and the convergence of the solutions. Statistical investigation is also performed by using the Friedman rank test and the Holm–Sidak multiple comparison $t$ - test to check the significance of the algorithm for its suitability to the robot trajectory optimization problems. The results show the significance of TLBO, ABC, and CS for the robot trajectory optimization problems.