As Unmanned Aerial Vehicles (UAVs) are widely used in many applications, a lot of military missions in confrontational environments are being undertaken by UAV swarm rather than human beings due to its advantages. In confrontational environments, the reliability and availability of UAV swarm would be the major concern because of UAVs’ vulnerability, so damage-tolerant task assigning algorithms are of great importance. In this paper, we come up with a novel damage-tolerant framework for assigning real-time tasks to UAVs with dynamical states in confrontational environments. Different from existing scheduling methods, we not only assign tasks but also back up copies of tasks to UAVs when needed, to promote reliability. Meanwhile, we adopt an overlapping mechanism, including Backup-Primary overlapping and Backup-Backup overlapping, in assignment to save the limited swarm resources. On the basis of the damage-tolerant and overlapping mechanism, for the first time, we propose a new damage-tolerant task assignment algorithm named DTTA, aiming at promoting the task success probability. Extensive experiments are conducted based on random synthetic workloads to compare DTTA with three baseline algorithms. The experimental results indicate that DTTA can efficiently promote the probability of tasks’ success without affecting the effectiveness of swarms in confrontational environments.

中文翻译：

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对抗环境下无人机群的容灾任务分配算法

由于无人驾驶飞机（UAV）广泛用于许多应用中，由于其优势，UAV机群而不是人类正在对抗环境中执行许多军事任务。在对抗环境中，由于无人机的脆弱性，无人机群的可靠性和可用性将成为主要问题，因此，容灾任务分配算法非常重要。在本文中，我们提出了一个新颖的容错框架，用于在对抗环境中为具有动态状态的无人机分配实时任务。与现有的调度方法不同，我们不仅分配任务，而且在需要时还将任务副本备份到无人机，以提高可靠性。同时，我们采用了一种重叠机制，包括Backup-Primary重叠和Backup-Backup重叠，以节省有限的群集资源。在容错和重叠机制的基础上，我们首次提出了一种新的容错任务分配算法DTTA，旨在提高任务成功率。基于随机的合成工作量进行了广泛的实验，以将DTTA与三种基线算法进行比较。实验结果表明，DTTA可以有效地提高任务成功的可能性，而不会影响群体在对抗环境中的效力。基于随机的合成工作量进行了广泛的实验，以将DTTA与三种基线算法进行比较。实验结果表明，DTTA可以有效地提高任务成功的可能性，而不会影响群体在对抗环境中的效力。基于随机的合成工作量进行了广泛的实验，以将DTTA与三种基线算法进行比较。实验结果表明，DTTA可以有效地提高任务成功的可能性，而不会影响群体在对抗环境中的效力。