Robot Path Planning Optimization Based on Multiobjective Grey Wolf Optimizer

摘要

For the environment of robot motion, workspace consisted of the positions and shapes of obstacles, optimization for robot operations requires not only one criteria but also several criteria. In this paper, a novel multi-objective method for optimal robot path planning is proposed based on Grey wolf optimizer (GWO). Two criteria of distance and smooth path of the robot path planning issue are transformed into a minimization one for fitness function. The position of the globally best agent in each iterative can be reached by the robot in sequence permutation. Series simulations are implemented in different static environments for the optimal path when the robot reaches its target. The results show that the proposed method provides the robot reaches its target with colliding free obstacles and the alternative method of optimization for robot planning.