Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring
Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring
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DOI：
10.1016/j.ast.2022.107494
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发表时间：
2022-03-18
影响因子：
5.6
通讯作者：
Arvin, Farshad
中科院分区：
文献类型：
--
作者：
Hu, Junyan;Niu, Hanlin;Arvin, Farshad
无人机群体协调问题因其在搜救、协同探测、目标监视等领域的广泛应用而备受关注。基于无人机的灵活性和基于图的协同控制策略的最新进展，本文旨在开发一种网络化无人机的容错协作框架，并将其应用于森林火灾监测。首先，提出了一种基于网络图论的协同导航策略，用于在存在未知干扰的情况下协调群中所有连接的无人机。利用李亚普诺夫方法保证了群系统的稳定性。在无人机执行任务期间，如果某些无人机的执行器损坏，则采用分散的任务重新分配算法，从而使无人机群对不确定性具有更强的鲁棒性。最后，提出了一种新的基于几何信息的碰撞避免方法，该方法利用船上的感知信息来避免在执行任务时可能发生的碰撞。首先通过仿真验证了该框架的有效性和可行性，然后通过室外环境下的真实飞行测试验证了该框架的有效性和可行性。(C)2022年爱思唯尔·马森公司。版权所有。
Coordination of unmanned aerial vehicle (UAV) swarms has received significant attention due to its wide practical applications including search and rescue, cooperative exploration and target surveillance. Motivated by the flexibility of the UAVs and the recent advancement of graph-based cooperative control strategies, this paper aims to develop a fault-tolerant cooperation framework for networked UAVs with applications to forest fire monitoring. Firstly, a cooperative navigation strategy based on network graph theory is proposed to coordinate all the connected UAVs in a swarm in the presence of unknown disturbances. The stability of the aerial swarm system is guaranteed using the Lyapunov approach. In case of damage to the actuators of some of the UAVs during the mission, a decentralized task reassignment algorithm is then applied, which makes the UAV swarm more robust to uncertainties. Finally, a novel geometry-based collision avoidance approach using onboard sensory information is proposed to avoid potential collisions during the mission. The effectiveness and feasibility of the proposed framework are verified initially by simulations and then using real-world flight tests in outdoor environments. (C) 2022 Elsevier Masson SAS. All rights reserved.