An improved PSO-based approach with dynamic parameter tuning for cooperative target searching of multi-robots

Multi-robot cooperation for target searching in completely unknown environments is a challenging topic that receives increasing attentions. In this paper, a novel potential field-based particle swarm optimization (PPSO) approach is applied for a team of mobile robots to cooperatively search for and reach targets in completely unknown environments. The target locations are unknown, where the robots explore the area and find the targets in a reasonable and effective way. The potential field function is the fitness function of the PSO, which is used to evaluate the exploration priority of the unknown area. The cooperation rules are defined in the proposed approach to lead the multi-robot system to explore the unknown environment. In addition, the district-difference degree and dynamic parameter tuning is added in the improved PPSO approach (IPPSO) to help the multi-robot system to complete complex tasks. The parameter setting is discussed in the simulation studies, and the effects of the parameter tuning is demonstrated by the experiment results.