Wang Gang, Lv Xiao, Yan Xiaohu
College of Computer Engineering, Naval University of Engineering, Wuhan 430033, China.
School of Undergraduate Education, Shenzhen Polytechnic University, Shenzhen 518055, China.
Sensors (Basel). 2023 Sep 20;23(18):7980. doi: 10.3390/s23187980.
Multi-UAV systems have been widely used in reconnaissance, disaster relief, communication, and other fields. However, many dynamic events can cause a partial failure of the original mission during the mission execution process, in which case task reassignment should be carried out. How to reassign resources and tasks in multi-dynamic, multi-target, and multi-constraint events becomes a core issue in the enhancement of combat efficiency. This paper establishes a model of multi-UAV cooperative reconnaissance task reassignment that comprehensively considers various dynamic factors such as UAV performance differences, size of target areas, and time window constraints. Then, a two-stage distributed task assignment algorithm (TS-DTA) is presented to achieve multi-task reassignment in dynamic environments. Finally, this paper verifies the effectiveness of the TS-DTA algorithm through simulation experiments and analyzes its performance through comparative experiments. The experimental results show that the TS-DTA algorithm can efficiently solve the task reassignment problem in dynamic environments while effectively reducing the communication burden of UAV formations.
多无人机系统已广泛应用于侦察、救灾、通信等领域。然而，在任务执行过程中，许多动态事件可能导致原任务部分失败，在这种情况下应进行任务重新分配。如何在多动态、多目标和多约束事件中重新分配资源和任务，成为提高作战效能的核心问题。本文建立了一个综合考虑无人机性能差异、目标区域大小和时间窗口约束等各种动态因素的多无人机协同侦察任务重新分配模型。然后，提出了一种两阶段分布式任务分配算法（TS-DTA），以实现动态环境下的多任务重新分配。最后，本文通过仿真实验验证了TS-DTA算法的有效性，并通过对比实验分析了其性能。实验结果表明，TS-DTA算法能够有效解决动态环境下的任务重新分配问题，同时有效降低无人机编队的通信负担。
