Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm

Abstract

Given that the traditional optimization algorithm (GWO) often encounters problems like local optimum, and the convergence efficiency is not satisfactory in the path planning task of multiple mobile robots, an improved grey wolf optimization algorithm (LAPGWO) based on the combination of the logistic chaotic mapping and the artificial potential field method (APF) is proposed. Firstly, the LAPGWO algorithm uses logistic chaotic mapping to initialize the scale of grey wolves, improving the diversity of the population distribution. Secondly, the potential field function of APF is introduced to guide the individual grey wolves to move towards the low potential energy area. By adjusting the angle between the resultant force direction of the possible field and the movement direction, the global search ability is enhanced, and the algorithm is prevented from falling into the local optimum. At the same time, in the later iterations, it gradually decreases to increase the local search ability and accelerate the search efficiency. Finally, a repulsive force correction term function is proposed to solve the problem of unreachable targets. An independent potential field is constructed for each robot during the driving process to reduce path conflicts. To verify the performance of the improved algorithm, this paper will verify and analyze two different improved grey wolf algorithms based on the warehouse environment. The results show that, compared with the GWO algorithm, the shortest path and calculation time of the LAPGWO algorithm is shortened by 22.09%, 34.12%, and 47.75%, respectively. It has better convergence and stability. A physical verification platform is built to verify the practical effectiveness of the method proposed in this paper.