Journal of Radiation Research and Radiation Processing, Volume. 40, Issue 6, 060601(2022)

Path planning for nuclear radiation environments based on an improved artificial potential field A* algorithm

Mengwen QIU1,2, Hua ZHANG1,2、*, and Huaifang ZHOU1,2
Author Affiliations
  • 1Southwest University of Science & Technology, Mianyang 621010, China
  • 2Sichuan Key Laboratory of Special Enviromental Robotics, Mianyang 621010, China
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    In order to enhance the path planning application of the transfer robot in a nuclear radiation environment, this paper addresses the problem of lack of influence from the surrounding environment on the path by the traditional A* algorithm, and introduces the idea of an artificial potential field in the nuclear radiation environment to improve the evaluation function of this algorithm. This was achieved by obtaining the sum of the radiation source repulsion function, obstacle repulsion function, and target point gravitational force function to calculate the combined force received in the grid, thereby ensuring that the planned path is far from the surrounding environment. For the improved A* algorithm, the MATLAB simulation experiments showed a decrease in the nuclear radiation dose by 52.11%~55.29% compared with the traditional A* algorithm, and a decrease in the number of search nodes by 34.67%~46.19% compared with other improved algorithms. Moreover, the safety distance with the radiation sources and surrounding obstacles was constantly maintained, providing a better implementation effect and allowing the verification of the effectiveness and superiority of the improved A* algorithm.

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    Mengwen QIU, Hua ZHANG, Huaifang ZHOU. Path planning for nuclear radiation environments based on an improved artificial potential field A* algorithm[J]. Journal of Radiation Research and Radiation Processing, 2022, 40(6): 060601

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    Paper Information

    Category: Research Articles

    Received: Jun. 3, 2022

    Accepted: Sep. 7, 2022

    Published Online: Jan. 4, 2023

    The Author Email: ZHANG Hua (swustai@163.com)

    DOI:10.11889/j.1000-3436.2022-0054

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