Experiments in unmanned aerial vehicle/unmanned ground vehicle radiation search
Experiments in unmanned aerial vehicle/unmanned ground vehicle radiation search
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DOI：
10.1002/rob.21867
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发表时间：
2019-06-01
影响因子：
8.3
通讯作者：
McLean, Morgan
中科院分区：
文献类型：
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作者：
Peterson, John;Li, Weilin;McLean, Morgan
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，以测试的性能的几种算法的定位放射性物质。在这个多机器人系统中，一个无人驾驶飞行器，一个定制的六翼直升机，和一个无人驾驶地面车辆（UGV），ClearPath豺狼，配备了伽马射线光谱仪，被用来收集来自两个放射源配置的数据。傅里叶散射变换和拉普拉斯特征映射算法的源检测上收集的数据集进行了测试。这些算法将原始光谱测量转换为备用空间，以允许聚类来检测指示放射源存在的数据内的趋势。本文还提出了一种点源模型和相应的信息论主动探测算法。现场测试验证了该模型融合空中和地面收集的辐射测量的能力，以及探索算法选择信息行动以减少模型不确定性的能力，使UGV能够在线定位放射性物质。
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.