针对未知环境下多无人机(unmanned aerial vehicles, UAV)协同搜索问题展开研究，通过网格化的环境规则描述，给出了UAV的运动规则及UAV系统的预测模型. 考虑实际战术需求，建立了满足UAV机动性能约束的区域再次进入数学模型，给出了最短再入路径的求解方法. 引入了“伸缩式”协同搜索机制，增加对未知环境的
适应性，同时设计了系统的全局信息库，从而充分利用UAV实时探测的环境信息，实施在线滚动规划. 通过仿真验证了方法的有效性.
This paper addresses a cooperative search problem where a team of unmanned aerial vehicles (UAVs) seeks to reduce uncertainty in an unknown environment. The gird-based representation of search environment is established, and a UAV dynamic model and a predictive model of UAV systems are presented.Taking into account the practical tactical requirements, an area re-enter model is formulated, considering some maneuverability constraints. The corresponding shortest re-enter path is formulated. We develop a cooperative search strategy based on “retractable” mechanism, which can enhance adaptability to unknown environments. We also propose a global information base to make full use of the real-time local environmental information
detected by the UAVs, and generate the search path on-line in a rolling style. Simulation results are presented to demonstrate the main feature of the proposed method.
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