A distributed multi-robot collaborative collision avoidance hunting method under probabilistic uncertainty framework

This paper proposes a novel method for multi-robot collision avoidance during a hunting task, within a probabilistic uncertainty framework. First, to minimize the total hunting time, this paper transforms the multi-robot hunting task assignment issue to a multi-objective problem by considering some necessary factors that may affect hunting efficiency, including distance, the number of obstacles, and the adaptation between pursuers and evaders. Then, an improved K-means clustering algorithm is proposed to allocate the pursuers to evaders, and the auction algorithm is designed to solve the multi-objective problem. Additionally, by taking into account the positional uncertainty of robots and obstacles, the Buffered Uncertainty-Aware Voronoi Cells (BUAVC) of robots are constructed to guarantee the probabilistic conditional anti-collision measures between robots, as well as between robots and obstacles. In the Buffered Uncertainty-Aware Voronoi hunting framework, a greedy switch pursuer control strategy is designed to enhance hunting capability, which minimizes hunting time as much as possible while satisfying the probability anti-collision condition and considering the `deadlock' problem. Finally, simulation experiments are conducted to illustrate the superiority of the proposed strategy with shorter global hunting time and total travel distance by comparing it with other existing methods.