Optimized trajectory planning of a robotic arm using teaching learning based optimization (TLBO) and artificial bee colony (ABC) optimization techniques

Abstract

In this paper, Teaching Learning Based Optimization (TLBO) algorithm, a recently developed advanced optimization technique, and Artificial Bee Colony (ABC) optimization techniques are applied to optimize the robot trajectory for a 3R robotic arm. The considered problem presents the multi-objective optimization with the objective to plan a trajectory which can minimize joint travelling time, joint travelling distance and total joint Cartesian lengths simultaneously. Six different design variables are considered for the joint angles, joint velocities and time from initial to intermediate and from intermediate to final positions. Results of TLBO and ABC are compared with the published results of Genetic Algorithm (GA). Comparison shows the better performance of TLBO and ABC over GA for the considered trajectory optimization problem. Moreover, experimentation is considered for different movement of robotic arm in the workspace by using TLBO and ABC. The result shows the better performance of TLBO over ABC in terms of best solutions, mean solutions, worst solutions and convergence.