WOA-AGA Algorithm Design for Robot Path Planning

Abstract

Currently, mobile robot has great application value in industrial production. It can play a unique advantage in improving industrial production efficiency and saving industrial production costs. Path planning plays an important role in the performance of mobile robots. Therefore, to improve the path planning efficiency of mobile robots in complex environments, a path planning model combining genetic algorithm (GA) and whale optimization algorithm (WOA), namely WOAAGA model, is proposed. In the model, the traditional GA model is introduced into the difference degree function. WOA makes up for the local optimization problem and the low proficiency of AGA algorithm. WOA-AGA effectively solves the problems of local optimization, long convergence time and unstable optimization results. The experiment is simulated in dynamic and static environment: AGA algorithm has 1.87% higher efficiency than GA algorithm; Compared with AGA algorithm, the overall operation efficiency of WOA-AGA algorithm is increased by 3.87%. Finally, two types of complex scenes are selected for path planning in the experiment. The results indicate that WOAAGA algorithm can obtain shorter and more reasonable optimal path than other similar algorithms. From the perspective of improving the path planning effect of mobile robots, this study aims to obtain the best path through the reasonable application of WOA-AGA model to improve industrial production efficiency.