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A distributed multi-robot collaborative collision avoidance hunting method under probabilistic uncertainty framework
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AI
DOI：10.1007/s11432-024-4534-2.png)
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Abstract
En 中文
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.
Keywords:
multi-robot hunting
Voronoi cell
collision avoidance
probabilistic uncertainty
multi-objective task assignment
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7.6
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Citations:
8.9K
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