Cooperation between an unmanned aerial vehicle and an unmanned ground vehicle in highly accurate localization of gamma radiation hotspots

This article discusses the highly autonomous robotic search and localization of radiation sources in outdoor environments. The cooperation between a human operator, an unmanned aerial vehicle, and an unmanned ground vehicle is used to render the given mission highly effective, in accordance with the idea that the search for potential radiation sources should be fast, precise, and reliable. Each of the components assumes its own role in the mission; the unmanned aerial vehicle (in our case, a multirotor) is responsible for fast data acquisition to create an accurate orthophoto and terrain map of the zone of interest. Aerial imagery is georeferenced directly, using an onboard sensor system, and no ground markers are required. The unmanned aerial vehicle can also perform rough radiation measurement, if necessary. Since the map contains three-dimensional information about the environment, algorithms to compute the spatial gradient, which represents the rideability, can be designed. Based on the primary aerial map, the human operator defines the area of interest to be examined by the applied unmanned ground vehicle carrying highly sensitive gamma-radiation probe/probes. As the actual survey typically embodies the most time-consuming problem within the mission, major emphasis is put on optimizing the unmanned ground vehicle trajectory planning; however, the dual-probe (differential) approach to facilitate directional sensitivity also finds use in the given context. The unmanned ground vehicle path planning from the pre-mission position to the center of the area of interest is carried out in the automated mode, similarly to the previously mentioned steps. Although the human operator remains indispensable, most of the tasks are performed autonomously, thus substantially reducing the load on the operator to enable them to focus on other actions during the search mission. Although gamma radiation is used as the demonstrator, most of the proposed algorithms and tasks are applicable on a markedly wider basis, including, for example, chemical, biological, radiological, and nuclear missions and environmental measurement tasks.

[1]  Tomas Jilek Radiation intensity mapping in outdoor environments using a mobile robot with RTK GNSS , 2015, International Conference on Military Technologies (ICMT) 2015.

[2]  Farhad Samadzadegan,et al.  EVALUATING THE POTENTIAL OF RTK-UAV FOR AUTOMATIC POINT CLOUD GENERATION IN 3D RAPID MAPPING , 2016 .

[3]  Syed Naeem Ahmed,et al.  Physics and Engineering of Radiation Detection , 2007 .

[4]  L. Klingbeil,et al.  DEVELOPMENT AND EVALUATION OF A UAV BASED MAPPING SYSTEM FOR REMOTE SENSING AND SURVEYING APPLICATIONS , 2015 .

[5]  Robin R. Murphy,et al.  A man-packable unmanned surface vehicle for radiation localization and forensics , 2015, 2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[6]  John S. Fardoulis,et al.  The use of unmanned aerial systems for the mapping of legacy uranium mines. , 2015, Journal of environmental radioactivity.

[7]  Isaac Amidror,et al.  Scattered data interpolation methods for electronic imaging systems: a survey , 2002, J. Electronic Imaging.

[8]  Ales Jelinek,et al.  Precise Multi-Sensor Georeferencing System for Micro UAVs , 2016 .

[9]  Anders la Cour-Harbo,et al.  Calibration and accuracy assessment in a direct georeferencing system for UAS photogrammetry , 2018 .

[10]  Jon Louis Bentley,et al.  Quad trees a data structure for retrieval on composite keys , 1974, Acta Informatica.

[11]  Alban Grastien,et al.  The JPS Pathfinding System , 2012, SOCS.

[12]  Jonathan S. Maltz,et al.  Measurement of the Energy-Dependent Angular Response of the ARES Detector System and Application to Aerial Imaging , 2017, IEEE Transactions on Nuclear Science.

[13]  Alexander Barzilov,et al.  Remote sensing of neutron and gamma radiation using aerial unmanned autonomous system , 2015, 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[14]  Ludek Zalud,et al.  Multispectral Stereoscopic Robotic Head Calibration and Evaluation , 2015, MESAS.

[15]  Ronald Lumia,et al.  Smart radiation sensor management , 2008, IEEE Robotics & Automation Magazine.

[16]  Robin R. Murphy,et al.  Run the robot backward , 2013, 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[17]  Atef Mohany,et al.  Development of a semi-autonomous directional and spectroscopic radiation detection mobile platform , 2015 .

[18]  P. Barry,et al.  FIELD ACCURACY TEST OF RPAS PHOTOGRAMMETRY , 2013 .

[19]  Tomás Jílek Pokročilá navigace v heterogenních multirobotických systémech ve vnějším prostředí ; Advanced Navigation in Heterogeneous Multi-robot Systems in Outdoor Environment , 2015 .

[20]  Stefano Caselli,et al.  Unmanned aerial vehicle equipped with spectroscopic CdZnTe detector for detection and identification of radiological and nuclear material , 2015, 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[21]  Mark R. Morelande,et al.  Information driven search for point sources of gamma radiation , 2010, Signal Process..

[22]  Hsien-I Lin,et al.  Searching a radiological source by a mobile robot , 2015, 2015 International Conference on Fuzzy Theory and Its Applications (iFUZZY).

[23]  Charles D. Ferguson,et al.  Commercial Radioactive Sources: Surveying the Security Risks , 2003 .

[24]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[25]  Arko Lucieer,et al.  Direct Georeferencing of Ultrahigh-Resolution UAV Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.