Comparison of Two Meta –Heuristic Algorithms for Path Planning in Robotics

Abstract

Path planning for robots is essential to seek the most feasible path due to power requirement, environmental factors and other limitations. The Grey wolf optimization (GWO) is a metaheuristic algorithm that is widely used for optimization of different parameters in the discrete search space to solve the various problems. Here in this paper, we have used improved version of Grey Wolf Optimization known as Variable Weight Grey Wolf Optimization (VM-GWO) for path planning of robots. The VM-GWO was implemented using two different 3 dimensional maps in order to obtain the better path planning. The results of VM-GWO were compared with the conventional GWO in terms of speed and shortest distance covered. The result demonstrates the better performance of VM-GWO in comparison to GWO.