Radiological Scouting, Monitoring and Inspection Using Drones

Luís Ramos Pinto, Alberto Vale, Yoeri Brouwer, Jorge Borbinha, José Corisco, Rodrigo Ventura, Ana Margarida Silva, André Mourato, Gonçalo Marques, Yuri Romanets, Susana Sargento, Bruno Gonçalves
<span title="2021-04-30">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="" style="color: black;">Sensors</a> </i> &nbsp;
Human populations and natural ecosystems are bound to be exposed to ionizing radiation from the deposition of artificial radionuclides resulting from nuclear accidents, nuclear devices or radiological dispersive devices ("dirty bombs"). On the other hand, Naturally Occurring Radioactive Material industries such as phosphate production or uranium mining, contribute to the on site storage of residuals with enhanced concentrations of natural radionuclides. Therefore, in the context of the European
more &raquo; ... agreements concerning nuclear energy, namely the European Atomic Energy Community Treaty, monitoring is an essential feature of the environmental radiological surveillance. In this work, we obtain 3D maps from outdoor scenarios, and complete such maps with measured radiation levels and with its radionuclide signature. In such scenarios, we face challenges such as unknown and rough terrain, limited number of sampled locations and the need for different sensors and therefore different tasks. We propose a radiological solution for scouting, monitoring and inspecting an area of interest, using a fleet of drones and a controlling ground station. First, we scout an area with a Light Detection and Ranging sensor onboard a drone to accurately 3D-map the area. Then, we monitor that area with a Geiger-Müller Counter at a low-vertical distance from the ground to produce a radiological (heat)map that is overlaid on the 3D map of the scenario. Next, we identify the hotspots of radiation, and inspect them in detail using a drone by landing on them, to reveal its radionuclide signature using a Cadmium-Zinc-Telluride detector. We present the algorithms used to implement such tasks both at the ground station and on the drones. The three mission phases were validated using actual experiments in three different outdoor scenarios. We conclude that drones can not only perform the mission efficiently, but in general they are faster and as reliable as personnel on the ground.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.3390/s21093143</a> <a target="_blank" rel="external noopener" href="">pmid:33946574</a> <a target="_blank" rel="external noopener" href="">fatcat:atze6zugwvcqzlr7dh4txoep2q</a> </span>
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