Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Autonomous exploration for radioactive sources localization based on radiation field reconstruction

  • Xulin Hu (Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology) ;
  • Junling Wang (School of National Defense Science, Southwest University of Science and Technology) ;
  • Jianwen Huo (Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology) ;
  • Ying Zhou (Mianyang Central Hospital, NHC Key Laboratory of Nuclear Technology Medical Transformation) ;
  • Yunlei Guo (Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology) ;
  • Li Hu (Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology)
  • Received : 2023.05.17
  • Accepted : 2023.11.09
  • Published : 2024.04.25
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Abstract

In recent years, unmanned ground vehicles (UGVs) have been used to search for lost or stolen radioactive sources to avoid radiation exposure for operators. To achieve autonomous localization of radioactive sources, the UGVs must have the ability to automatically determine the next radiation measurement location instead of following a predefined path. Also, the radiation field of radioactive sources has to be reconstructed or inverted utilizing discrete measurements to obtain the radiation intensity distribution in the area of interest. In this study, we propose an effective source localization framework and method, in which UGVs are able to autonomously explore in the radiation area to determine the location of radioactive sources through an iterative process: path planning, radiation field reconstruction and estimation of source location. In the search process, the next radiation measurement point of the UGVs is fully predicted by the design path planning algorithm. After obtaining the measurement points and their radiation measurements, the radiation field of radioactive sources is reconstructed by the Gaussian process regression (GPR) model based on machine learning method. Based on the reconstructed radiation field, the locations of radioactive sources can be determined by the peak analysis method. The proposed method is verified through extensive simulation experiments, and the real source localization experiment on a Cs-137 point source shows that the proposed method can accurately locate the radioactive source with an error of approximately 0.30 m. The experimental results reveal the important practicality of our proposed method for source autonomous localization tasks.

Keywords

Acknowledgement

This research was funded by the National Natural Science Foundation of China (No.12205245), Natural Science Foundation of Sichuan Province (No.2023NSFSC1437), NHC Key Laboratory of Nuclear Technology Medical Transformation (Mianyang Central Hospital) (No. 2021HYX16), and State Administration of Science, Technology and Industry for National Defense (No. JCKY2020404C001).

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