Multi-agv path planning for indoor factory by using prioritized planning and improved ant algorithm

Abstract

Multiple automated guided vehicle (multi-AGV) path planning in manufacturing workshops has always been technically difficult for industrial applications. This paper presents a multi-AGV path planning method based on prioritized planning and improved ant colony algorithms. Firstly, in dealing with the problem of path coordination between AGVs, an improved priority algorithm is introduced, where priority is assigned based on the remaining battery charge of the AGVs, which improves the power usage efficiency of the AGVs. Secondly, an improved ant colony algorithm (IAC) is proposed to calculate the optimal path for the AGVs. In the algorithm, a random amount of pheromone is distributed in the map and the amount of pheromone is updated according to a fitness value. As a result, the computational efficiency of the ant colony algorithm is improved. Moreover, a mutation operation is introduced to mutate the amount of pheromone in randomly selected locations of the map, by which the problem of local optimum is well overcome. Simulation results and a comparative analysis showed the validity of the proposed method.