As the rapid development of control, computer and artificial intelligence as well as the complexity of demand, multi-robot systems play a more and more important role because of the advantages of distribution, flexibility, robustness. Multi-robot coordination and control have become an important research direction in robotics. This paper considers multi-robot hunting task coordination and target recognition, which are as follows: Firstly, multi-robot systems are introduced, and several representative multiple robots systems and typical simulation platforms are described. The current research work on task allocation, multi-robot hunting, and target recognition are summarized. We also give the background and content arrangement of the paper. Secondly, a hierarchical task allocation method based on task case is proposed. Based on a layered organization, the method combines the task cases with history information, contract net, and acquaintance net together. The proposed method is tested in multi-robot hunting simulation. Thirdly, for the case of more than one evader in multi-robot hunting, after the robotic teams to pursue the specific evaders are determined by the allocation method above mentioned, we propose a hunting strategy with dynamic alliance based on circular-elliptical besieging circle for a better performance, and give the alliance conditions. The simulations are conducted to verify the proposed strategy. Fourthly, a target recognition method with threshold dynamic adjustment based on reference lights is given. After calibrating all thresholds of colors in different reference lights, we adjust the thresholds of current image to get the belongings of all pixels. On the basis of colors, the targets are recognized with shape information. Finally, the research work is concluded and the future research is described.
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