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Vision-based multi-agent cooperative target search

机译:基于视觉的多主体合作目标搜索

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This paper addresses vision-based cooperative search for multiple mobile ground targets by a group of unmanned aerial vehicles (UAVs) with limited sensing and communication capabilities. The airborne camera on each UAV has a limited field of view and its target discriminability varies as a function of altitude. First, a general target detection probability model is built based on the physical imaging process of a camera. By dividing the whole surveillance region into cells, a probability map can be formed for each UAV indicating the probability of target existence within each cell. Then, we propose a distributed probability map updating model which includes the fusion of measurement information, information sharing among neighboring agents, information decaying and transmission due to environmental changes such as the target movement. Furthermore, we formulate the target search problem by multiple agents as a cooperative coverage control problem by optimizing the collective coverage area and the detection performance. The proposed map updating model and the cooperative control scheme are distributed, i.e., assuming that each agent only communicates with its neighbors within its communication range. Finally, the effectiveness of the proposed algorithms is illustrated by simulation.
机译:本文介绍了一组视觉和协作能力有限的无人飞行器(UAV)对多个移动地面目标进行基于视觉的协作搜索。每个无人机上的机载摄像机的视野有限,其目标可辨别性随高度而变化。首先,基于照相机的物理成像过程建立一般目标检测概率模型。通过将整个监视区域划分为多个单元,可以为每个UAV形成一个概率图,指示每个单元中目标存在的概率。然后,我们提出了一种分布式概率图更新模型,该模型包括测量信息的融合,相邻代理之间的信息共享,信息衰减以及由于环境变化(例如目标运动)而导致的传递。此外,我们通过优化集体覆盖区域和检测性能,将多个代理的目标搜索问题表述为合作覆盖控制问题。所提议的地图更新模型和协作控制方案是分布式的,即,假设每个代理仅在其通信范围内与其邻居进行通信。最后,通过仿真说明了所提算法的有效性。

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