Probabilistic search with agile UAVs
Probabilistic search with agile UAVs
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DOI：
10.1109/robot.2010.5509962
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发表时间：
2010-05
期刊：
影响因子：
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通讯作者：
S. Waharte;A. Symington;A. Trigoni
中科院分区：
文献类型：
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作者：
S. Waharte;A. Symington;A. Trigoni
通过快速获取航空图像的能力，无人机(UAV)具有帮助目标搜索任务的潜力。许多用于计划搜索任务的核心算法使用基于占用网格的表示法，并且通常基于两个主要假设。首先，无人机的高度是恒定的。其次，车载传感器可以测量整个网格单元的整个状态。尽管这些假设对于固定翼、高速无人机来说是足够的，但我们认为它们不适合于小型、轻型、低速和灵活的无人机，如四旋翼无人机。这些平台具有改变高度的能力，其低速度意味着多个测量可能很容易在相当长的一段时间内与多个单元重叠。在本文中，我们扩展了一种基于决策的概率搜索框架，将网格单元的多个观测和无人机高度的变化结合在一起。我们考虑了完全和部分覆盖多个网格单元的观测区域。我们通过大量的仿真实例展示了所产生的影响。
Through their ability to rapidly acquire aerial imagery, Unmanned Aerial Vehicles (UAVs) have the potential to aid target search tasks. Many of the core algorithms which are used to plan search tasks use occupancy grid-based representations and are often based on two main assumptions. Firstly, the altitude of the UAV is constant. Secondly, the onboard sensors can measure the entire state of an entire grid cell. Although these assumptions are sufficient for fixed-wing, high speed UAVs, we do not believe that they are appropriate for small, lightweight, low speed and agile UAVs such as quadrotors. These platforms have the ability to change altitude and their low speed means that multiple measurements may easily overlap multiple cells for substantial periods of time. In this paper we extend a framework for probabilistic search based on decision making to incorporate multiple observations of grid cells and changes in UAV altitude. We account for observation areas that completely and partially cover multiple grid cells. We show the resultant impact on a number of simulation examples.