面向海上目标搜索任务的多无人机协同航路优化

提出了一种面向多无人机(unmanned aerial vehicle,UAV)协同搜索海上目标任务的航路优化方法。首先,分析多无人机协同航路优化问题的基本要素模型。然后,在对各UAV独立维护的目标概率图信息进行探测更新的基础上,采用状态预测一致性算法实现目标概率图信息的快速融合。最后,同时考虑局部搜索收益与未来搜索收益,采用分布式模型预测控制(distributed model predictive control,DMPC)方法优化各UAV的搜索航路。仿真结果表明,本研究提出的方法具有较高的搜索效率,可有效应用于海上目标的快速搜索任务,具有重要的应用价值。

Cooperative path optimization of multi-UAVs when searching for maritime targets

In this paper, we solve the cooperative path optimization problem of multiple unmanned aerial vehicles (UAVs) when searching for maritime targets. First, we analyze the problem in detail by modeling its basic elements. Next, based on updated results from the detection process, we use the consensus theory with a state predictor to fuse the UAV target probability maps. Lastly, we use distributed model predictive control (DMPC) to optimize the UAV searching routes and simultaneously introduce local and future searching rewards. The simulation results indicate that the searching efficiency of our proposed method is higher than that achieved by current techniques.