Nuclear Engineering and Technology

  • 제55권5호
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  • Pages.1838-1854
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  • 2023
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Multi-objective path planning for mobile robot in nuclear accident environment based on improved ant colony optimization with modified A*

  • De Zhang (School of Resource Environment and Safety Engineering, University of South China) ;
  • Run Luo (School of Resource Environment and Safety Engineering, University of South China) ;
  • Ye-bo Yin (School of Computer Science, University of South China) ;
  • Shu-liang Zou (Hunan Provincial Key Laboratory of Emergency Safety Technology and Equipment for Nuclear Facilities )
  • 투고 : 2022.08.19
  • 심사 : 2023.02.03
  • 발행 : 2023.05.25
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초록

This paper presents a hybrid algorithm to solve the multi-objective path planning (MOPP) problem for mobile robots in a static nuclear accident environment. The proposed algorithm mimics a real nuclear accident site by modeling the environment with a two-layer cost grid map based on geometric modeling and Monte Carlo calculations. The proposed algorithm consists of two steps. The first step optimizes a path by the hybridization of improved ant colony optimization algorithm-modified A* (IACO-A*) that minimizes path length, cumulative radiation dose and energy consumption. The second module is the high radiation dose rate avoidance strategy integrated with the IACO-A* algorithm, which will work when the mobile robots sense the lethal radiation dose rate, avoiding radioactive sources with high dose levels. Simulations have been performed under environments of different complexity to evaluate the efficiency of the proposed algorithm, and the results show that IACO-A* has better path quality than ACO and IACO. In addition, a study comparing the proposed IACO-A* algorithm and recent path planning (PP) methods in three scenarios has been performed. The simulation results show that the proposed IACO-A* IACO-A* algorithm is obviously superior in terms of stability and minimization the total cost of MOPP.

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과제정보

This work was supported by the Scientific Research Foundation of Hunan Provincial Education Department of China (Grant No. 19B502) and the Basic Scientific research project of National Defense of China (Grant No. JCKY2019403D003) and the authors would like to thank the comments from the anonymous reviewers that improved the quality of the paper.

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