Nuclear Engineering and Technology

  • 제51권3호
  • /
  • Pages.825-836
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  • 2019
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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DL-RRT* algorithm for least dose path Re-planning in dynamic radioactive environments

  • Chao, Nan (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) ;
  • Liu, Yong-kuo (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) ;
  • Xia, Hong (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) ;
  • Peng, Min-jun (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) ;
  • Ayodeji, Abiodun (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University)
  • 투고 : 2018.07.29
  • 심사 : 2018.11.30
  • 발행 : 2019.04.25
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초록

One of the most challenging safety precautions for workers in dynamic, radioactive environments is avoiding radiation sources and sustaining low exposure. This paper presents a sampling-based algorithm, DL-RRT*, for minimum dose walk-path re-planning in radioactive environments, expedient for occupational workers in nuclear facilities to avoid unnecessary radiation exposure. The method combines the principle of random tree star ($RRT^*$) and $D^*$ Lite, and uses the expansion strength of grid search strategy from $D^*$ Lite to quickly find a high-quality initial path to accelerate convergence rate in $RRT^*$. The algorithm inherits probabilistic completeness and asymptotic optimality from $RRT^*$ to refine the existing paths continually by sampling the search-graph obtained from the grid search process. It can not only be applied to continuous cost spaces, but also make full use of the last planning information to avoid global re-planning, so as to improve the efficiency of path planning in frequently changing environments. The effectiveness and superiority of the proposed method was verified by simulating radiation field under varying obstacles and radioactive environments, and the results were compared with $RRT^*$ algorithm output.

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참고문헌

  1. A. Alzalloum, Application of Shortest Path Algorithms to Find Paths of Minimum Radiation Dose, Dissertation, Urbana, Illinois, 2009.
  2. M.A. Khasawneh, M.I. Malkawi, T.S. Hayajneh, A graph-coloring-based navigational algorithm for personal safety in nuclear applications, ISMA (2009) 1-7.
  3. M.A. Khasawneh, Z.A.M. Al-Shboul, M.A. Jaradat, A localized navigation algorithm for radiation evasion for nuclear facilities: optimizing the "Radiation Evasion" criterion: Part I, Nucl. Eng. Des. 259 (2013) 240-257. https://doi.org/10.1016/j.nucengdes.2011.09.034
  4. M.A. Khasawneh, Z.A.M. Al-Shboul, M.A. Jaradat, A localized navigation algorithm for radiation evasion for nuclear facilities. Part II: optimizing the "Nearest Exit" criterion, Nucl. Eng. Des. 259 (2013) 258-277. https://doi.org/10.1016/j.nucengdes.2011.10.057
  5. K. Chizhov, M.K. Sneve, S. Shinkarev, A. Tsovyanov, et al., Methods of minimising doses incurred by external exposure while moving in radiation hazardous areas, J. Radiol. Prot. 37 (2017) 697-714. https://doi.org/10.1088/1361-6498/aa7c4f
  6. M.K. Li, Y.K. Liu, M.J. Peng, C.L. Xie, et al., Dynamic minimum dose pathsearching method for virtual nuclear facilities, Prog. Nucl. Energy 91 (2016) 1-8. https://doi.org/10.1016/j.pnucene.2016.04.001
  7. Y.K. Liu, M.K. Li, C.L. Xie, et al., Path-planning research in radioactive environment based on particle swarm algorithm, Prog. Nucl. Energy 74 (2014) 184-192. https://doi.org/10.1016/j.pnucene.2014.03.002
  8. Z. Wang, J. Cai, The path-planning in radioactive environment of nuclear facilities using an improved particle swarm optimization algorithm, Nucl. Eng. Des. 326 (2018) 79-86. https://doi.org/10.1016/j.nucengdes.2017.11.006
  9. S. Karaman, E. Frazzoli, Sampling-based algorithms for optimal motion planning, Int. J. Robot Res. 30 (No. 7) (2011) 846-894. https://doi.org/10.1177/0278364911406761
  10. N. Chao, Y.K. Liu, H. Xia, C.L. Xie, A. Ayodeji, H. Yang, L. Bai, A sampling-based method with virtual reality technology to provide minimum dose path navigation for occupational workers in nuclear facilities, Prog. Nucl. Energy 100 (2017) 22-32. https://doi.org/10.1016/j.pnucene.2017.05.024
  11. N. Chao, Y.K. Liu, H. Xia, A. Ayodeji, L. Bai, Grid-based RRT* for minimum dose walking path-planning in complex radioactive environments, Ann. Nucl. Energy 115 (2018) 73-82. https://doi.org/10.1016/j.anucene.2018.01.007
  12. S.M. Persson, I. Sharf, Sampling-based A* algorithm for robot path-planning, Int. J. Robot Res. 33 (No. 13) (2014) 1683-1708. https://doi.org/10.1177/0278364914547786
  13. M. Otte, E. Frazzoli, RRTX: asymptotically optimal single-query samplingbased motion planning with quick replanning, Int. J. Robot Res. 35 (No. 7) (2016) 797-822. https://doi.org/10.1177/0278364915594679
  14. S. Koenig, M. Likhachev, D.* Lite, Eighteenth National Conference on Artificial Intelligence, Menlo Park, CA, USA, 2002, pp. 476-483.

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  3. Hybrid Assembly Path Planning for Complex Products by Reusing a Priori Data vol.9, pp.4, 2021, https://doi.org/10.3390/math9040395
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  5. Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite vol.11, pp.17, 2019, https://doi.org/10.3390/app11177863
  6. Metaheuristic minimum dose path planning for nuclear power plant decommissioning vol.166, 2019, https://doi.org/10.1016/j.anucene.2021.108800
  7. Minimum dose path planning based on three-degree vertex algorithm and FLUKA modeling: Radiation source discrimination and shielding considerations vol.168, 2019, https://doi.org/10.1016/j.anucene.2021.108916