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On November 14, 2025 at 9:24:46 AM UTC,
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Added resource Optimal Path Planning in a Real-World Radioactive Environment: A Comparative Study of A-star and Dijkstra Algorithms to Optimal Path Planning in a Real-World Radioactive Environment: A Comparative Study of A-star and Dijkstra Algorithms
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| 2 | "author": null, | 2 | "author": null, | ||
| 3 | "author_email": null, | 3 | "author_email": null, | ||
| 4 | "creator": [ | 4 | "creator": [ | ||
| 5 | { | 5 | { | ||
| 6 | "email": "", | 6 | "email": "", | ||
| 7 | "name": "Miyombo Ernest Miyombo" | 7 | "name": "Miyombo Ernest Miyombo" | ||
| 8 | }, | 8 | }, | ||
| 9 | { | 9 | { | ||
| 10 | "email": "", | 10 | "email": "", | ||
| 11 | "name": "Yongkuo Liu" | 11 | "name": "Yongkuo Liu" | ||
| 12 | }, | 12 | }, | ||
| 13 | { | 13 | { | ||
| 14 | "email": "", | 14 | "email": "", | ||
| 15 | "name": "Chishinga Milton Mulenga" | 15 | "name": "Chishinga Milton Mulenga" | ||
| 16 | }, | 16 | }, | ||
| 17 | { | 17 | { | ||
| 18 | "email": "", | 18 | "email": "", | ||
| 19 | "name": "Anthony Siamulonga" | 19 | "name": "Anthony Siamulonga" | ||
| 20 | }, | 20 | }, | ||
| 21 | { | 21 | { | ||
| 22 | "email": "", | 22 | "email": "", | ||
| 23 | "name": "Martin Chihango Kabanda" | 23 | "name": "Martin Chihango Kabanda" | ||
| 24 | }, | 24 | }, | ||
| 25 | { | 25 | { | ||
| 26 | "email": "", | 26 | "email": "", | ||
| 27 | "name": "Phillimon Shaba" | 27 | "name": "Phillimon Shaba" | ||
| 28 | }, | 28 | }, | ||
| 29 | { | 29 | { | ||
| 30 | "email": "", | 30 | "email": "", | ||
| 31 | "name": "Chunli Xi" | 31 | "name": "Chunli Xi" | ||
| 32 | }, | 32 | }, | ||
| 33 | { | 33 | { | ||
| 34 | "email": "", | 34 | "email": "", | ||
| 35 | "name": "Abiodun Ayodej" | 35 | "name": "Abiodun Ayodej" | ||
| 36 | } | 36 | } | ||
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| 42 | "language": "en", | 42 | "language": "en", | ||
| 43 | "license_id": "notspecified", | 43 | "license_id": "notspecified", | ||
| 44 | "license_title": "License not specified", | 44 | "license_title": "License not specified", | ||
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| 47 | "metadata_created": "2025-11-14T09:13:39.372192", | 47 | "metadata_created": "2025-11-14T09:13:39.372192", | ||
| n | 48 | "metadata_modified": "2025-11-14T09:21:32.892789", | n | 48 | "metadata_modified": "2025-11-14T09:24:46.456434", |
| 49 | "name": | 49 | "name": | ||
| 50 | dradioactiveenvironmentacomparativestudyofastaranddijkstraalgorithms", | 50 | dradioactiveenvironmentacomparativestudyofastaranddijkstraalgorithms", | ||
| 51 | "no_pii_confirmation": "1", | 51 | "no_pii_confirmation": "1", | ||
| 52 | "notes": "Navigating complex radioactive environments while | 52 | "notes": "Navigating complex radioactive environments while | ||
| 53 | minimizing radiation exposure to workers is a critical challenge faced | 53 | minimizing radiation exposure to workers is a critical challenge faced | ||
| 54 | by the nuclear industry. Although various shortest-path algorithms and | 54 | by the nuclear industry. Although various shortest-path algorithms and | ||
| 55 | radiation dose calculation techniques have been employed for optimal | 55 | radiation dose calculation techniques have been employed for optimal | ||
| 56 | path finding, most existing models are based on simulations that do | 56 | path finding, most existing models are based on simulations that do | ||
| 57 | not accurately represent real-world environments. To address this | 57 | not accurately represent real-world environments. To address this | ||
| 58 | limitation, this study presents a path-planning experiment conducted | 58 | limitation, this study presents a path-planning experiment conducted | ||
| 59 | on a naturally\r\nradioactive slag dump, Slag Dump No. 48, also known | 59 | on a naturally\r\nradioactive slag dump, Slag Dump No. 48, also known | ||
| 60 | as Black Mountain, in Zambia. The experiment utilizes the Radiation | 60 | as Black Mountain, in Zambia. The experiment utilizes the Radiation | ||
| 61 | Detection Backpack System (RDBS) and Geolocation Application for | 61 | Detection Backpack System (RDBS) and Geolocation Application for | ||
| 62 | Radiation Monitoring (GARM) in conjunction with the Dijkstra and | 62 | Radiation Monitoring (GARM) in conjunction with the Dijkstra and | ||
| 63 | A-star algorithms to search for an optimal walking path on the slag | 63 | A-star algorithms to search for an optimal walking path on the slag | ||
| 64 | dump. The distances between neighboring nodes and heuristic values, | 64 | dump. The distances between neighboring nodes and heuristic values, | ||
| 65 | derived from gamma dose rates, are experimentally obtained from the | 65 | derived from gamma dose rates, are experimentally obtained from the | ||
| 66 | GARM software. This research contributes to the field by: (1) | 66 | GARM software. This research contributes to the field by: (1) | ||
| 67 | performing a realworld path planning experiment on a radioactive slag | 67 | performing a realworld path planning experiment on a radioactive slag | ||
| 68 | dump, (2) applying RDBS for measuring gamma radiation from a naturally | 68 | dump, (2) applying RDBS for measuring gamma radiation from a naturally | ||
| 69 | radioactive slag, (3) investigating the combined use of RDBS, GARM, | 69 | radioactive slag, (3) investigating the combined use of RDBS, GARM, | ||
| 70 | Dijkstra, and A-star algorithms for optimal path finding, (4) | 70 | Dijkstra, and A-star algorithms for optimal path finding, (4) | ||
| 71 | generating heuristic values and node distances experimentally for path | 71 | generating heuristic values and node distances experimentally for path | ||
| 72 | planning in an actual radioactive environment, and (5) comparing the | 72 | planning in an actual radioactive environment, and (5) comparing the | ||
| 73 | performance of state-of-the-art minimum dose walking path algorithms | 73 | performance of state-of-the-art minimum dose walking path algorithms | ||
| 74 | on dose rate-based and node distance-based weighted graphs. The | 74 | on dose rate-based and node distance-based weighted graphs. The | ||
| 75 | results of this study and the proposed future work provide valuable | 75 | results of this study and the proposed future work provide valuable | ||
| 76 | insights for enhancing radiation protection and optimizing path | 76 | insights for enhancing radiation protection and optimizing path | ||
| 77 | planning in radioactive environments. ", | 77 | planning in radioactive environments. ", | ||
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| 82 | "created": "2024-08-30T16:51:11.400197", | 82 | "created": "2024-08-30T16:51:11.400197", | ||
| 83 | "description": "Zambia National Institute for Scientific and | 83 | "description": "Zambia National Institute for Scientific and | ||
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| 113 | "description": "Navigating complex radioactive environments | ||||
| 114 | while minimizing radiation exposure to workers is a critical challenge | ||||
| 115 | faced by the nuclear industry. Although various shortest-path | ||||
| 116 | algorithms and radiation dose calculation techniques have been | ||||
| 117 | employed for optimal path finding, most existing models are based on | ||||
| 118 | simulations that do not accurately represent real-world environments. | ||||
| 119 | To address this limitation, this study presents a path-planning | ||||
| 120 | experiment conducted on a naturally radioactive slag dump, Slag Dump | ||||
| 121 | No. 48, also known as Black Mountain, in Zambia. The experiment | ||||
| 122 | utilizes the Radiation Detection Backpack System (RDBS) and | ||||
| 123 | Geolocation Application for Radiation Monitoring (GARM) in conjunction | ||||
| 124 | with the Dijkstra and A-star algorithms to search for an optimal | ||||
| 125 | walking path on the slag dump. The distances between neighboring nodes | ||||
| 126 | and heuristic values, derived from gamma dose rates, are | ||||
| 127 | experimentally obtained from the GARM software. This research | ||||
| 128 | contributes to the field by: (1) performing a realworld path planning | ||||
| 129 | experiment on a radioactive slag dump, (2) applying RDBS for measuring | ||||
| 130 | gamma radiation from a naturally radioactive slag, (3) investigating | ||||
| 131 | the combined use of RDBS, GARM, Dijkstra, and A-star algorithms for | ||||
| 132 | optimal path finding, (4) generating heuristic values and node | ||||
| 133 | distances experimentally for path planning in an actual radioactive | ||||
| 134 | environment, and (5) comparing the performance of state-of-the-art | ||||
| 135 | minimum dose walking path algorithms on dose rate-based and node | ||||
| 136 | distance-based weighted graphs. The results of this study and the | ||||
| 137 | proposed future work provide valuable insights for enhancing radiation | ||||
| 138 | protection and optimizing path planning in radioactive environments.", | ||||
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| 108 | "rights": "Miyombo Ernest Miyombo, Yong-kuo Liu, Chishinga Milton | 162 | "rights": "Miyombo Ernest Miyombo, Yong-kuo Liu, Chishinga Milton | ||
| 109 | Mulenga, Anthony Siamulonga, Martin Chihango Kabanda, Phillimon Shaba, | 163 | Mulenga, Anthony Siamulonga, Martin Chihango Kabanda, Phillimon Shaba, | ||
| 110 | Chunli Xi, Abiodun Ayodeji", | 164 | Chunli Xi, Abiodun Ayodeji", | ||
| 111 | "source": [ | 165 | "source": [ | ||
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| 117 | "notes": "" | 171 | "notes": "" | ||
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| 120 | "state": "draft", | 174 | "state": "draft", | ||
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| 122 | { | 176 | { | ||
| 123 | "display_name": "A-star Algorithm", | 177 | "display_name": "A-star Algorithm", | ||
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| 167 | "title": "Optimal Path Planning in a Real-World Radioactive | 221 | "title": "Optimal Path Planning in a Real-World Radioactive | ||
| 168 | Environment: A Comparative Study of A-star and Dijkstra Algorithms", | 222 | Environment: A Comparative Study of A-star and Dijkstra Algorithms", | ||
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