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首页> 外文期刊>Aerospace science and technology >Potential field based receding horizon motion planning for centrality-aware multiple UAV cooperative surveillance
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Potential field based receding horizon motion planning for centrality-aware multiple UAV cooperative surveillance

机译：基于电位场的后视地平线运动规划，用于集中性感知的多无人机协同监视

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In this paper, we propose a two-layer control framework for the cooperative surveillance problem using multiple Unmanned Aerial Vehicles (UAVs). The framework consists of a network topology control layer and a motion planning layer. The former regulates the network topology and maintains the network connectivity. The latter plans the motion of UAVs using the distributed receding horizon optimization. The model of the cooperative searching problem is built based on the probability of targets and the detection history of UAVs over the region. The forgotten factor is introduced to drive the UAVs to revisit the areas that have been searched before. Furthermore, the tradeoff between the coverage enhancement and the network performance is achieved by taking into account the centrality of communication links in the deletion of communication links. The potential field design in the receding horizon optimization is presented to obtain the optimal motion of UAVs without violating the collision avoidance and network connectivity constraints. Simulation results demonstrate the feasibility of the proposed methods by analyzing the effects of the forgotten factor and the centrality of communication links. (C) 2015 Elsevier Masson SAS. All rights reserved,
机译：在本文中，我们为使用多个无人机的协同监视问题提出了两层控制框架。该框架由网络拓扑控制层和运动计划层组成。前者调节网络拓扑并维护网络连接。后者使用分布式后视地平线优化计划无人机的运动。基于目标概率和区域内无人机的探测历史，建立了协同搜索问题的模型。引入了被遗忘的因素，以驱动无人机重新访问之前搜索过的区域。此外，通过在删除通信链路中考虑通信链路的中心性来实现覆盖增强和网络性能之间的折衷。提出了后视优化中的势场设计，以在不违反避免碰撞和网络连接约束的前提下获得无人机的最佳运动。仿真结果通过分析被遗忘因素的影响和通信链接的中心性，证明了所提方法的可行性。 （C）2015 Elsevier Masson SAS。版权所有，

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