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首页> 外文期刊>Journal of Robotic Systems >Experiments in unmanned aerial vehicle/unmanned ground vehicle radiation search
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Experiments in unmanned aerial vehicle/unmanned ground vehicle radiation search

机译:无人机/地面无人机辐射搜索实验

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This paper discusses the results of a field experiment conducted at Savannah River National Laboratory to test the performance of several algorithms for the localization of radioactive materials. In this multirobot system, both an unmanned aerial vehicle, a custom hexacopter, and an unmanned ground vehicle (UGV), the ClearPath Jackal, equipped with gamma-ray spectrometers, were used to collect data from two radioactive source configurations. Both the Fourier scattering transform and the Laplacian eigenmap algorithms for source detection were tested on the collected data sets. These algorithms transform raw spectral measurements into alternate spaces to allow clustering to detect trends within the data which indicate the presence of radioactive sources. This study also presents a point source model and accompanying information-theoretic active exploration algorithm. Field testing validated the ability of this model to fuse aerial and ground collected radiation measurements, and the exploration algorithm's ability to select informative actions to reduce model uncertainty, allowing the UGV to locate radioactive material online.
机译:本文讨论了在萨凡纳河国家实验室进行的现场实验的结果,以测试几种用于放射性物质定位的算法的性能。在这种多机器人系统中,无人驾驶飞机,定制的六翼飞机和无人驾驶地面车辆(ClearPath Jackal)都配备了伽马射线谱仪,用于收集来自两种放射源配置的数据。在收集的数据集上测试了用于源检测的傅里叶散射变换和Laplacian特征图算法。这些算法将原始光谱测量值转换为替代空间,以允许聚类来检测数据中指示放射性源存在的趋势。这项研究还提出了一个点源模型和附带的信息理论主动探索算法。现场测试验证了该模型融合空中和地面收集的辐射测量值的能力,以及探索算法选择信息活动以减少模型不确定性的能力,从而使UGV能够在线定位放射性物质。

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