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Unmanned vehicles, equipped with radiation detection sensors, can serve as a valuable aid to personnel responding to radiological incidents. The use of tele-operated ground vehicles avoids human exposure to hazardous environments, which in addition to radioactive contamination, might present other risks to personnel. Autonomous unmanned vehicles using algorithms for radioisotope classification, source localization, and efficient exploration allow these vehicles to conduct surveys with reduced human supervision allowing teams to address larger areas in less time. This work presents systems for autonomous radiation search with results presented in several proof-of-concept demonstrations. 

Abstract 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 withγ‐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.