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In response to the challenges of radiation source localization in shielded environments， a combination-driven algorithm is proposed based on an improved simultaneous perturbation stochastic approximation (SPSA) framework. The proposed algorithm enhances the convergence performance by optimizing one-sided orthogonal perturbation vectors and incorporating a dynamic step-size adjustment strategy. To address path infeasibility in complex environments， a goal relocation mechanism based on breadth-first search (BFS) is introduced. For complex multi-space scenarios with shielding structures， an active exploration strategy is employed to proactively investigate unobserved regions. To evaluate the performance of the proposed algorithm in terms of convergence performance， obstacle adaptability， and exploration capacity， three simulation environments are set up in Gazebo， which respectively represent the following environments: open-field environment， complex environment and radiation shielding environment. The experiments demonstrate that the proposed algorithm outperforms the standard SPSA algorithm and particle filter algorithm in terms of convergence performance， while also exhibiting robust search capability in complex environments. Furthermore， when confronted with shielding environments， the proposed algorithm demonstrates the capability of actively exploring unknown areas within a relatively short period after being obstructed.
[1] XIAO W. Current status and safeguard measures of nuclear industry development [J]. Papermaking Equipment & Materials， 2020， 49(2): 252. (in Chinese)
[2] MIERNICKI E A， HEALD A L， HUFF K D， et al. Nuclear waste heat use in agriculture: history and opportunities in the United States [J]. Journal of Cleaner Production， 2020， 267: 121918.
[3] HATCH M， RON E， BOUVILLE A， et al. The Chernobyl disaster: cancer following the accident at the Chernobyl nuclear power plant [J]. Epidemiologic Reviews， 2005， 27: 56-66.
[4] NAGATANI K， KIRIBAYASHI S， OKADA Y， et al. Emergency response to the nuclear accident at the Fukushima Daiichi Nuclear Power Plants using mobile rescue robots [J]. Journal of Field Robotics， 2013， 30(1): 44-63.
[5] FENG C， WANG C Z， ZHAO J P， et al. Research status and key technologies analysis of operating robots for nuclear environment [J]. Opto-Electronic Engineering， 2020， 47(10): 88-98. (in Chinese)
[6] CHRISTIE G A， SHOEMAKER A， KOCHERSBERGER K B， et al. Radiation search operations using scene understanding with autonomous UAV and UGV [J]. Journal of Field Robotics， 2017， 34(8): 1450-1468.
[7] LI Z G， CHEN W H， LIU C J. Review of UAV-based autonomous search algorithms for hazardous sources [J]. Scientia Sinica (Informationis)， 2022， 52(9): 1579-1597.
[8] XU T L， CHEN G D， ZHOU G Q， et al. Fast source seeking with obstacle avoidance via extremum seeking control [C]//2022 13th Asian Control Conference (ASCC). New York: IEEE， 2022: 2097-2102.
[9] RAMíREZ-LLANOS E， MARTíNEZ S. Stochastic source seeking for mobile robots in obstacle environments via the SPSA method [J]. IEEE Transactions on Automatic Control， 2019， 64(4): 1732-1739.
[10] CHEN G D， YUAN S， ZHU X Z， et al. Path planning for fast swarm source seeking in unknown environments [J]. International Journal of Adaptive Control and Signal Processing， 2024， 38(2): 360-377.
[11] WEST A， TSITSIMPELIS I， LICATA M， et al. Use of Gaussian process regression for radiation mapping of a nuclear reactor with a mobile robot [J]. Scientific Reports， 2021， 11(1): 13975.
[12] ZHANG C， XIAO Y F， LIU H J， et al. Search method of radioactive source based on particle filter and artificial potential field [J]. Nuclear Physics Review， 2020， 37(4): 867-874.
[13] HUTCHINSON M， LIU C J， THOMAS P， et al. Unmanned aerial vehicle-based hazardous materials response: information-theoretic hazardous source search and reconstruction [J]. IEEE Robotics & Automation Magazine， 2020， 27(3): 108-119.
[14] ZHU H B. Research on source search and localization with mobile robots in unknown environments [D]. Harbin: Harbin Institute of Technology， 2022. (in Chinese)
[15] LONG Z Y， LIU Y K， YANG L Q， et al. Study on localization method of radioactive source based on point kernel integration [J]. Applied Science and Technology， 2020， 47(6): 78-83.
[16] TAN Y， YANG J， CHEN W H， et al. Multistep dual control for exploration and exploitation in autonomous search with convergence guarantee [J]. IEEE Transactions on Industrial Informatics， 2024， 20(6): 8207-8217.
[17] LI Z G， CHEN W H， YANG J. Concurrent active learning in autonomous airborne source search: dual control for exploration and exploitation [J]. IEEE Transactions on Automatic Control， 2023， 68(5): 3123-3130.
[18] PROKHORETSI M， PROKHORETS S I， KHAZHMURADOV M A， et al. Point kernel method for radiation fields simulation [J]. Nuclear Physics Investigations， 2007， 78(48): 106-109.
Basic Information:
DOI：10.19884/j.1672-5220.202507002
China Classification Code:TL77
Citation Information:
[1]LI Guanru,HU Jun,YU Zhou ,et al.A Combination-Driven Radiation Source Seeking Algorithm Based on Improved SPSA[J].Journal of Donghua University (English Edition)().DOI:10.19884/j.1672-5220.202507002.
2026-04-27
2026-04-27
2026-04-27