Complete coverage path planning of nuclear radiation field using bio-inspired neural network
Path planning for the complete coverage of nuclear radiation fields is necessary to ensure the radiation safety of regional operators in radiation environments. Based on a bio-inspired neural network algorithm, a complete coverage path-planning algorithm for the optimal control of the radiation dose...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | zho |
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Science Press
2024-02-01
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| Series: | Fushe yanjiu yu fushe gongyi xuebao |
| Subjects: | |
| Online Access: | http://www.fs.sinap.ac.cn/thesisDetails#10.11889/j.1000-3436.2023-0093&lang=zh |
| _version_ | 1797292836906860544 |
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| author | LUO Zhaojin LIU Chengfeng JIA Wenbao SHAN Qing SHI Chao ZHANG Jiandong HEI Daqian ZHANG Xiaojun LING Yongsheng |
| author_facet | LUO Zhaojin LIU Chengfeng JIA Wenbao SHAN Qing SHI Chao ZHANG Jiandong HEI Daqian ZHANG Xiaojun LING Yongsheng |
| author_sort | LUO Zhaojin |
| collection | DOAJ |
| description | Path planning for the complete coverage of nuclear radiation fields is necessary to ensure the radiation safety of regional operators in radiation environments. Based on a bio-inspired neural network algorithm, a complete coverage path-planning algorithm for the optimal control of the radiation dose is proposed. First, part of the terrain of the Fukushima nuclear power plant and the Monte Carlo particle transport program were used to construct the obstacle distribution and radiation dose field in a simulated nuclear radiation field. Subsequently, the Python programming language was used to conduct algorithm simulation experiments. Each grid of the simulated nuclear radiation field was defined as a neuron, and a bio-inspired neural network was established. The grid dose rate and neuronal activity were combined to achieve optimal control of radiation dose in path planning, and single, four, and eight mobile units were used for simulation experiments. The results showed that the planning path of a single mobile unit can achieve 100% coverage and a 4% coverage repetition rate and can first cover the low-dose area and delay the coverage of the high-dose area to achieve optimal control of the process and cumulative doses. The algorithm is improved via a multiunit collaborative search to increase the time efficiency of complete coverage and decrease the cumulative dose of monomers. The coverage repetition rates of four-unit and eight-unit simulations were 5.72% and 6.29%, respectively. The complete coverage times of the one-unit, four-unit, and eight-unit simulations were 30, 9, and 4 min, respectively, and the time efficiency was doubled. The maximum cumulative doses of the monomers for the one-unit, four-unit, and eight-unit simulations were 4.11×10-3, 1.28×10-3, and 0.85×10-3 mSv, respectively, which also decreased significantly. The proposed algorithm can achieve complete coverage path planning of optimal control of the process dose and cumulative dose. Moreover, the algorithm can coordinate multiunit path planning and significantly decrease the cumulative dose of monomers, which is critical for radiation protection during regional operations in a radiation environment. |
| first_indexed | 2024-03-07T20:03:19Z |
| format | Article |
| id | doaj.art-9fd460bb11be41819c43453496b5f297 |
| institution | Directory Open Access Journal |
| issn | 1000-3436 |
| language | zho |
| last_indexed | 2024-03-07T20:03:19Z |
| publishDate | 2024-02-01 |
| publisher | Science Press |
| record_format | Article |
| series | Fushe yanjiu yu fushe gongyi xuebao |
| spelling | doaj.art-9fd460bb11be41819c43453496b5f2972024-02-28T05:31:41ZzhoScience PressFushe yanjiu yu fushe gongyi xuebao1000-34362024-02-0142101060101060110.11889/j.1000-3436.2023-00931000-3436（2024）01-0085-14Complete coverage path planning of nuclear radiation field using bio-inspired neural networkLUO Zhaojin0LIU Chengfeng1JIA Wenbao2SHAN Qing3SHI Chao4ZHANG Jiandong5HEI Daqian6ZHANG Xiaojun7LING Yongsheng8Institute of Nuclear Analysis Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaInstitute of Nuclear Analysis Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaInstitute of Nuclear Analysis Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaInstitute of Nuclear Analysis Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaInstitute of Nuclear Analysis Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaInstitute of Nuclear Analysis Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Nuclear Science and Technology, Lanzhou University, Nanjing 211106, ChinaSuzhou Guanrui Information Technology Co., Ltd., Suzhou 215008, ChinaInstitute of Nuclear Analysis Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaPath planning for the complete coverage of nuclear radiation fields is necessary to ensure the radiation safety of regional operators in radiation environments. Based on a bio-inspired neural network algorithm, a complete coverage path-planning algorithm for the optimal control of the radiation dose is proposed. First, part of the terrain of the Fukushima nuclear power plant and the Monte Carlo particle transport program were used to construct the obstacle distribution and radiation dose field in a simulated nuclear radiation field. Subsequently, the Python programming language was used to conduct algorithm simulation experiments. Each grid of the simulated nuclear radiation field was defined as a neuron, and a bio-inspired neural network was established. The grid dose rate and neuronal activity were combined to achieve optimal control of radiation dose in path planning, and single, four, and eight mobile units were used for simulation experiments. The results showed that the planning path of a single mobile unit can achieve 100% coverage and a 4% coverage repetition rate and can first cover the low-dose area and delay the coverage of the high-dose area to achieve optimal control of the process and cumulative doses. The algorithm is improved via a multiunit collaborative search to increase the time efficiency of complete coverage and decrease the cumulative dose of monomers. The coverage repetition rates of four-unit and eight-unit simulations were 5.72% and 6.29%, respectively. The complete coverage times of the one-unit, four-unit, and eight-unit simulations were 30, 9, and 4 min, respectively, and the time efficiency was doubled. The maximum cumulative doses of the monomers for the one-unit, four-unit, and eight-unit simulations were 4.11×10-3, 1.28×10-3, and 0.85×10-3 mSv, respectively, which also decreased significantly. The proposed algorithm can achieve complete coverage path planning of optimal control of the process dose and cumulative dose. Moreover, the algorithm can coordinate multiunit path planning and significantly decrease the cumulative dose of monomers, which is critical for radiation protection during regional operations in a radiation environment.http://www.fs.sinap.ac.cn/thesisDetails#10.11889/j.1000-3436.2023-0093&lang=zhbio-inspired neural networknuclear radiation fieldcomplete coverage path planningmulti-unit collaborationdose control |
| spellingShingle | LUO Zhaojin LIU Chengfeng JIA Wenbao SHAN Qing SHI Chao ZHANG Jiandong HEI Daqian ZHANG Xiaojun LING Yongsheng Complete coverage path planning of nuclear radiation field using bio-inspired neural network Fushe yanjiu yu fushe gongyi xuebao bio-inspired neural network nuclear radiation field complete coverage path planning multi-unit collaboration dose control |
| title | Complete coverage path planning of nuclear radiation field using bio-inspired neural network |
| title_full | Complete coverage path planning of nuclear radiation field using bio-inspired neural network |
| title_fullStr | Complete coverage path planning of nuclear radiation field using bio-inspired neural network |
| title_full_unstemmed | Complete coverage path planning of nuclear radiation field using bio-inspired neural network |
| title_short | Complete coverage path planning of nuclear radiation field using bio-inspired neural network |
| title_sort | complete coverage path planning of nuclear radiation field using bio inspired neural network |
| topic | bio-inspired neural network nuclear radiation field complete coverage path planning multi-unit collaboration dose control |
| url | http://www.fs.sinap.ac.cn/thesisDetails#10.11889/j.1000-3436.2023-0093&lang=zh |
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