关键词: 移动充电机器人, 路径规划, 灰狼算法, 栅格化地图

Abstract: The emergence of mobile charging robots has solved the problem of charging electric vehicles in old parking lots, but the complex and random obstacle environment in parking lots puts forward higher requirements for their path planning and obstacle avoidance functions. The path planning problem of an electric vehicle mobile charging robot in a parking lot was simulated and analyzed by improving the traditional gray wolf optimization algorithm. The gray wolf optimization algorithm has fast iteration speed, but low optimization accuracy, and is prone to falling into local optima. After rasterizing the parking lot map, the traditional gray wolf optimization algorithm was improved from the perspective of fitness function, convergence factor and location update function. Simulation using MATLAB showed that the average iteration number of the improved gray wolf optimization algorithm was reduced by 39.4 % compared to the traditional gray wolf optimization algorithm, and the path length was reduced by 4.7 %, the path length was the shortest compared with other typical improved gray wolf optimization algorithm. At the same time, the path of mobile charging robots with different occupancy rates in the same parking lot was simulated using this algorithm, and the results showed that the algorithm can successfully run under random maps and different target locations, verifying the stability of the algorithm.

Key words: mobile charging robot, path planning, gray wolf optimization algorithm, rasterized map