Resumen:
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[EN] Radioactive pollution detection plays a key role in nuclear technology application. In this paper, an array-type of nuclear pollution detection system is designed for the detection scenario of complex surfaces. Firstly, ...[+]
[EN] Radioactive pollution detection plays a key role in nuclear technology application. In this paper, an array-type of nuclear pollution detection system is designed for the detection scenario of complex surfaces. Firstly, to get the three-dimensional point cloud of the surface, a complex surface was modeled based on the geometric ranging model of a two-dimensional laser profilometer and the motion model of a two-degree-of-freedom displacement platform. Secondly, an 'S' type scanning scheme of profilometer was developed to overcome the problem of limited scanning area of the profilometer. Thirdly, Euclidean distance weighted median filtering was used to solve the impulsive noise that may occur during the point cloud acquisition process. Finally, the 3D point cloud information of the complex surface was used for controlling the movement of the 6 x 6 array channel pollution detector to complete the alpha and beta particle measurement tasks. A mechanical platform was constructed for experiments, the results are as follows. The working range of this system is from -5 cm to 5 cm in elevation difference of surfaces, and the accuracy is 12 mu m in surface height measuring. It takes 26.13 s to perform a detection task including surface scanning and the detector moving, and scanning accuracy is 0.35 x 0.35 mm(2). The maximum control error of the surface contamination detector is 0.4 mm. Specifically, the detection area of the system reaches 240 x 240 mm(2). The results show that the system acquires three-dimensional terrain information, and realizes control over the movement of the pollution detector accurately and then completes the detection of alpha and beta particles effectively.
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Agradecimientos:
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This work is supported by National Natural Science Foundation (NSFC) of China under Grant
No.61601382, Sichuan Provincial Science and Technology Support Project No.2019YJ0325, the Doctoral Fund of
Southwest University of ...[+]
This work is supported by National Natural Science Foundation (NSFC) of China under Grant
No.61601382, Sichuan Provincial Science and Technology Support Project No.2019YJ0325, the Doctoral Fund of
Southwest University of Science and Technology No.16zx7148, No.19zx7123, Longshan academic talent research
supporting program of SWUST No.18LZX632 and the Fund of Robot Technology Used for Special Environment
Key Laboratory of Sichuan Province No.13zxtk08.
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