Optimal Path Planning for Mobile Robots in Complex Environments Based on the Grey Wolf Algorithm and Self-powered Sensors

Abstract

The term "mobile robot" or "intelligent mobile robot" refers to an autonomous system with specific sensory skills that can move independently in an environment that contains obstacles and can perform certain activities, and belongs to the category of intelligent robots. The use of mobile robots combines expertise from many different academic fields, including strongly integrated techniques in biology, anthropology, computer science, control theory, power transmission, measurement, and sensing. At present, there are already cases of using mobile robots to perform various simple tasks, but due to the low efficiency of optimal path planning, the scope of application cannot be further expanded. Therefore, based on the improved gray wolf algorithm and self-powered sensor technology, this paper optimized the optimal path planning method and energy driving method of the mobile robot, so that the mobile robot can be applied in complex environments. The experiment compared the improved path planning algorithm in this paper with the traditional path planning algorithm. The results show that the optimal path planning using the improved gray wolf algorithm can search the random tree algorithm, the improved bee colony algorithm and the multi-objective based on edge distribution estimation faster. Compared with the optimization algorithm, the optimization effect was increased by 14.84%. It is beneficial to the optimal path planning of mobile robots in complex environments.