The coverage and detection limit of a sampling point for robotics radiation mapping

• Main Authors: Abd Rahman N.A., Mohamed Sahari K.S., Baharuddin M.Z.
• Other Authors: 57855445300
• Format: Article
• Published: Elsevier Ltd 2024
• Subjects: Detection limit; Mobile robot; Radiation mapping; Radiation sampling point; Sampling point coverage; Mobile robots; Detection limits; Hotspots; Point coverage; Radiation maps; Sampling points; Sensor coverage problem; Article; computer simulation; dosimetry; gamma irradiation; isotope analysis; limit of detection; mathematical computing; mathematical model; radiation detection; radiation dose; radiation field; radiation mapping; radiological parameters; robotics; sensitivity analysis; Mapping

The sensor coverage problem aims to maximize the coverage of a target area with a fixed or minimum number of sensors. However, the sampling point coverage for radiation mapping has yet to be specified or adequately established. When dealing with unknown radiation fields, it is critical that the placements of sampling points will ensure that all hotspots are detected and accurately identified. Therefore, the concept of coverage and detection limit for a sampling point in radiation mapping is proposed in this paper. The proposed concept relates the angular dependency of the radiation measurement instruments with the detector detection limit or minimum detectable amount (MDA). To demonstrate the implementation, the concept is used to compute the sensitivity of the radiation map for coverage radiation mapping with mobile robot. Simulation results showed that hotspots with intensity equal to or above the sampling point detection limit were successfully detected regardless of their position within the coverage circle. Moreover, the experimental results of coverage radiation mapping showed that the concept can be used to compute the resolution of the radiation map. This will help the user to efficiently configure the appropriate grid size that suit their mapping situation and requirements. � 2023 Elsevier Ltd