This paper proposes a cooperative search algorithm to enable swarms of unmanned aerial vehicles (UAVs) to capture moving targets. It is based on prior information and target probability constrained by inter-UAV distance for safety and communication. First, a rasterized environmental cognitive map is created to characterize the task area. Second, based on Bayesian theory, the posterior probability of a target’s existence is updated using UAV detection information. Third, the predicted probability distribution of the dynamic time-sensitive target is obtained by calculating the target transition probability. Fourth, a customized information interaction mechanism switches the interaction strategy and content according to the communication distance to produce cooperative decision-making in the UAV swarm. Finally, rolling-time domain optimization generates interactive information, so interactive behavior and autonomous decision-making among the swarm members are realized. Simulation results showed that the proposed algorithm can effectively complete a cooperative moving-target search when constrained by communication distance yet still cooperate effectively in unexpected situations such as a fire.

中文翻译：

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具有通信距离约束的无人机群协同目标搜索

本文提出了一种协作搜索算法，使成群的无人机 (UAV) 能够捕获移动目标。它基于先验信息和受无人机间距离约束的目标概率，用于安全和通信。首先，创建一个光栅化的环境认知地图来表征任务区域。其次，基于贝叶斯理论，利用无人机检测信息更新目标存在的后验概率。第三，通过计算目标转移概率得到动态时敏目标的预测概率分布。第四，定制化的信息交互机制根据通信距离切换交互策略和内容，在无人机群中产生协同决策。最后，滚动时域优化产生交互信息，实现群体成员之间的交互行为和自主决策。仿真结果表明，该算法在通信距离受限的情况下，能够有效完成协同运动目标搜索，在火灾等突发情况下仍能有效协同。