Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/28943
Title: Path planning in nuclear facility decommissioning: Research status, challenges, and opportunities
Authors: Adibeli, JO
Liu, Y-K
Ayodeji, A
Awodi, NJ
Keywords: nuclear decommissioning;path planning;radiation protection;algorithms;robotics;dose assessment
Issue Date: 4-Jun-2021
Publisher: Elsevier on behalf of the Korean Nuclear Society
Citation: Adibeli, J.O. et al. (2021) 'Path planning in nuclear facility decommissioning: Research status, challenges, and opportunities', Nuclear Engineering and Technology, 53 (11), pp. 3505 - 3516. doi: 10.1016/j.net.2021.05.038.
Abstract: During nuclear facility decommissioning, workers are continuously exposed to high-level radiation. Hence, adequate path planning is critical to protect workers from unnecessary radiation exposure. This work discusses recent development in radioactive path planning and the algorithms recommended for the task. Specifically, we review the conventional methods for nuclear decommissioning path planning, analyze the techniques utilized in developing algorithms, and enumerate the decision factors that should be considered to optimize path planning algorithms. As a major contribution, we present the quantitative performance comparison of different algorithms utilized in solving path planning problems in nuclear decommissioning and highlight their merits and drawbacks. Also, we discuss techniques and critical consideration necessary for efficient application of robots and robotic path planning algorithms in nuclear facility decommissioning. Moreover, we analyze the influence of obstacles and the environmental/radioactive source dynamics on algorithms' efficiency. Finally, we recommend future research focus and highlight critical improvements required for the existing approaches towards a safer and cost-effective nuclear-decommissioning project.
Description: Research data for this article: Data not available / No data was used for the research described in the article.
Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S1738573321003144#appsec1 .
URI: https://bura.brunel.ac.uk/handle/2438/28943
DOI: https://doi.org/10.1016/j.net.2021.05.038
ISSN: 1738-5733
Other Identifiers: ORCiD: Yong-kuo Liu https://orcid.org/0000-0001-7729-7154
ORCiD: Abiodun Ayodeji https://orcid.org/0000-0003-3257-7616
ORCiD: Ngbede Junior Awodi https://orcid.org/0000-0003-4772-1238
Appears in Collections:Brunel Innovation Centre

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