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DC Field | Value | Language |
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dc.contributor.author | Miyombo, ME | - |
dc.contributor.author | Liu, Y-K | - |
dc.contributor.author | Mulenga, CM | - |
dc.contributor.author | Siamulonga, A | - |
dc.contributor.author | Kabanda, MC | - |
dc.contributor.author | Shaba, P | - |
dc.contributor.author | Xi, C | - |
dc.contributor.author | Ayodeji, A | - |
dc.date.accessioned | 2024-05-14T17:00:20Z | - |
dc.date.available | 2024-05-14T17:00:20Z | - |
dc.date.issued | 2024-02-26 | - |
dc.identifier | ORCiD: Anthony Siamulonga https://orcid.org/0009-0002-4629-6196 | - |
dc.identifier | ORCiD: Phillimon Shaba https://orcid.org/0009-0001-7177-2433 | - |
dc.identifier | ORCiD: Abiodun Ayodeji https://orcid.org/0000-0003-3257-7616 | - |
dc.identifier | 113039 | - |
dc.identifier.citation | Miyombo, M.E. et al. (2024) 'Optimal path planning in a real-world radioactive environment: A comparative study of A-star and Dijkstra algorithms', Nuclear Engineering and Design, 420, 113039, pp. 1 - 10. doi: 10.1016/j.nucengdes.2024.113039. | en_US |
dc.identifier.issn | 0029-5493 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29004 | - |
dc.description | Data availability: Data will be made available on request. | en_US |
dc.description.abstract | Navigating complex radioactive environments while minimizing radiation exposure to workers is a critical challenge faced by the nuclear industry. Although various shortest-path algorithms and radiation dose calculation techniques have been employed for optimal path finding, most existing models are based on simulations that do not accurately represent real-world environments. To address this limitation, this study presents a path-planning experiment conducted on a naturally radioactive slag dump, Slag Dump No. 48, also known as Black Mountain, in Zambia. The experiment utilizes the Radiation Detection Backpack System (RDBS) and Geolocation Application for Radiation Monitoring (GARM) in conjunction with the Dijkstra and A-star algorithms to search for an optimal walking path on the slag dump. The distances between neighboring nodes and heuristic values, derived from gamma dose rates, are experimentally obtained from the GARM software. This research contributes to the field by: (1) performing a real-world path planning experiment on a radioactive slag dump, (2) applying RDBS for measuring gamma radiation from a naturally radioactive slag, (3) investigating the combined use of RDBS, GARM, Dijkstra, and A-star algorithms for optimal pathfinding, (4) generating heuristic values and node distances experimentally for path planning in an actual radioactive environment, and (5) comparing the performance of state-of-the-art minimum dose walking path algorithms on dose rate-based and node distance-based weighted graphs. The results of this study and the proposed future work provide valuable insights for enhancing radiation protection and optimizing path planning in radioactive environments. | en_US |
dc.description.sponsorship | Fundamental Research Funds for the Central Universities (NO. 3072022TS1501); the project of Institute of Computer Application, China Academy of Engineering Physics (NO. HT-2022-115); National Natural Science Foundation for Young Scientists of China (Grant No. 12205065); the project of China Institute for Radiation Protection (NO. CIRP-CNNCRPTKLJJ003); Heilongjiang Natural Science Foundation (joint guidance) (NO. LH2021F002); Fundamental Research Funds for the Central Universities (NO. 3072022JC0404). | en_US |
dc.format.extent | 1 - 10 | - |
dc.format.medium | Print-Electrlonic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Copyright © 2024 Elsevier. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ (see: https://www.elsevier.com/about/policies/sharing). | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | optimal path planning | en_US |
dc.subject | radiation dose assessment | en_US |
dc.subject | gamma radiation | en_US |
dc.subject | A-star algorithm | en_US |
dc.subject | Dijkstra algorithm | en_US |
dc.subject | nuclear decommissioning | en_US |
dc.title | Optimal path planning in a real-world radioactive environment: A comparative study of A-star and Dijkstra algorithms | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2024-02-20 | - |
dc.identifier.doi | https://doi.org/10.1016/j.nucengdes.2024.113039 | - |
dc.relation.isPartOf | Nuclear Engineering and Design | - |
pubs.publication-status | Published | - |
pubs.volume | 420 | - |
dc.identifier.eissn | 1872-759X | - |
dc.rights.license | https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.en | - |
dc.rights.holder | Elsevier | - |
Appears in Collections: | Brunel Innovation Centre |
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