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室内时变流场中多机器人源定位方法实验与数值验证

奉祁林 杨艺斌 邓烨 蔡浩 姜明瑞 鲁静雨 张灿鑫 张博远

奉祁林, 杨艺斌, 邓烨, 蔡浩, 姜明瑞, 鲁静雨, 张灿鑫, 张博远. 室内时变流场中多机器人源定位方法实验与数值验证[J]. 环境工程, 2021, 39(2): 73-81. doi: 10.13205/j.hjgc.202102012
引用本文: 奉祁林, 杨艺斌, 邓烨, 蔡浩, 姜明瑞, 鲁静雨, 张灿鑫, 张博远. 室内时变流场中多机器人源定位方法实验与数值验证[J]. 环境工程, 2021, 39(2): 73-81. doi: 10.13205/j.hjgc.202102012
FENG Qi-lin, YANG Yi-bin, DENG Ye, CAI Hao, JIANG Ming-rui, LU Jing-yu, ZHANG Can-xin, ZHANG Bo-yuan. EXPERIMENTAL AND NUMERICAL VALIDATION ON A MULTI-ROBOT SOURCE LOCALIZATION METHOD FOR DYNAMIC INDOOR ENVIRONMENT[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(2): 73-81. doi: 10.13205/j.hjgc.202102012
Citation: FENG Qi-lin, YANG Yi-bin, DENG Ye, CAI Hao, JIANG Ming-rui, LU Jing-yu, ZHANG Can-xin, ZHANG Bo-yuan. EXPERIMENTAL AND NUMERICAL VALIDATION ON A MULTI-ROBOT SOURCE LOCALIZATION METHOD FOR DYNAMIC INDOOR ENVIRONMENT[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(2): 73-81. doi: 10.13205/j.hjgc.202102012

室内时变流场中多机器人源定位方法实验与数值验证

doi: 10.13205/j.hjgc.202102012
基金项目: 

国家自然科学基金"受限空间中多个危险重气泄漏源的快速辨识问题研究"(51478468)。

详细信息
    作者简介:

    奉祁林(1990-),男,博士,工程师,主要从事室内污染源(或危险源)控制、重要经济目标防护、设施规划等方面的研究。qilinf2017@163.com

    通讯作者:

    蔡浩(1976-),男,博士,教授,主要从事室内污染物传播与控制、建筑环境安全、室内热湿环境营造等方面的研究。caihao@njtech.edu.cn

EXPERIMENTAL AND NUMERICAL VALIDATION ON A MULTI-ROBOT SOURCE LOCALIZATION METHOD FOR DYNAMIC INDOOR ENVIRONMENT

  • 摘要: 为了快速和准确定位室内时变流场中的污染源,提出了一种综合的多机器人源定位方法(URPSO),并综合利用机器人实验和数值实验来验证该方法的有效性。首先,在风扇左右周期性摆动的实验环境中,利用3台机器人开展了15组独立性实验,其中14组实验成功定位源,成功率达到93.3%,说明URPSO方法在室内时变流场中具有强鲁棒性。其次,针对真实实验环境,利用仿真方法开展了机器人源定位实验,结果表明:仿真方法和实验方法获得的成功率一致,且2种方法的平均定位步数和方差接近,说明利用仿真方法开展源定位研究是可行的。最后,利用仿真方法在3种典型室内时变流场环境(混合通风案例MV、置换通风案例DV和自然通风案例NV)中,分别开展了100组独立性实验,对应的成功率分别为100%、92%和81%,说明URPSO方法在不同时变流场环境中均具有较高成功率。
  • [1] JIN M, LIU S C, SCHIAVON S, et al. Automated mobile sensing:towards high-granularity agile indoor environmental quality monitoring[J]. Building and Environment, 2018, 127:268-276.
    [2] WORLD HEALTH ORGANIZATION. Household air pollution and heatlh[DB/OL]. https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health. 2018.
    [3] SZCZUREK A, DOLEGA A, MACIEJEWSKA M. Profile of occupant activity impact on indoor air-method of its determination[J]. Energy and Buildings, 2018, 158:1564-1575.
    [4] CAI H, LI X T, CHEN Z L, et al. Rapid identification of multiple constantly-released contaminant sources in indoor environments with unknown release time[J]. Building and Environment, 2014, 81:7-19.
    [5] ZHAI Z Q, LIU X, WANG H D, et al. Experimental verification of tracking algorithm for dynamically-releasing single indoor contaminant[J]. Building Simulation, 2012, 5(1):5-14.
    [6] WANG H D, LU S, CHENG J J, et al. Inverse modeling of indoor instantaneous airborne contaminant source location with adjoint probability-based method under dynamic airflow field[J]. Building and Environment, 2017, 117:178-190.
    [7] CHEN Y C, CAI H, CHEN Z L, et al. Using multi-robot active olfaction method to locate time-varying contaminant source in indoor environment[J]. Building and Environment, 2017, 118:101-112.
    [8] CHEN X X, HUANG J. Odor source localization algorithms on mobile robots:a review and future outlook[J]. Robotics and Autonomous Systems, 2019, 112:123-136.
    [9] VUKOVIC V, SREBRIC J. Application of neural networks trained with multizone models for fast detection of contaminant source position in buildings[J]. ASHRAE Transactions, 2007, 113(2):154-162.
    [10] ZHANG T F, CHEN Q. Identification of contaminant sources in enclosed environments by inverse CFD modeling[J]. Indoor Air, 2007, 17(3):167-177.
    [11] ZHANG T F, LI H Z, WANG S G. Inversely tracking indoor airborne particles to locate their release sources[J]. Atmospheric Environment, 2012, 55:328-338.
    [12] TAGADE P M, JEONG B M, CHOI H L. A Gaussian process emulator approach for rapid contaminant characterization with an integrated multizone-CFD model[J]. Building and Environment, 2013, 70:232-244.
    [13] SHAO X L, LI X T, MA H Y. Identification of constant contaminant sources in a test chamber with real sensors[J]. Indoor Built Environ, 2016, 25(6):997-1010.
    [14] XUE Y, ZHAI Z Q. Inverse identification of multiple outdoor pollutant sources with a mobile sensor[J]. Building Simulation, 2017, 10(2):255-263.
    [15] YANG Y B, FENG Q L, CAI H, et al. Experimental study on three single-robot active olfaction algorithms for locating contaminant sources in indoor environments with no strong airflow[J]. Building and Environment, 2019, 155:320-333.
    [16] 路光达, 张明路, 张小俊, 等. 机器人仿生嗅觉研究现状[J]. 天津工业大学学报, 2010, 29(6):72-77.
    [17] LILIENTHAL A J, LOUTFI A, DUCKETT T. Airborne chemical sensing with mobile robots[J]. Sensors, 2006, 6(11):1616-1678.
    [18] ISHIDA H, WADA Y, MATSUKURA H. Chemical sensing in robotic applications:a review[J]. IEEE Sensors Journal, 2012, 12(11):3163-3173.
    [19] KOWADLO G, RUSSELL R A. Robot odor localization:a taxonomy and survey[J]. The International Journal of Robotics Research, 2008, 27(8):869-894.
    [20] FENG Q L, CAI H, CHEN Z L, et al. Experimental study on a comprehensive particle swarm optimization method for locating contaminant sources in dynamic indoor environments with mechanical ventilation[J]. Engergy and Buildings, 2019, 196:145-156.
    [21] LUO D H. Multi-robot odor source localization strategy based on a modified ant colony algorithm[J]. Robot, 2008, 30(6):536-541.
    [22] MENG Q H, YANG W X, WANG Y, et al. Adapting an ant colony metaphor for multi-robot chemical plume tracing[J]. Sensors, 2012, 12(4):4737-4763.
    [23] 孟庆浩, 李飞, 张明路, 等. 湍流烟羽环境下多机器人主动嗅觉实现方法研究[J]. 自动化学报, 2008, 34(10):1281-1290.
    [24] MENG Q H, YANG W X, WANG Y, et al. Collective odor source estimation and search in time-variant airflow environments using mobile robots[J]. Sensors, 2011, 11(11):10415-10443.
    [25] MARQUES L, NUNES U, DE ALMEIDA A T. Particle swarm-based olfactory guided search[J]. Autonomous Robots, 2006, 20(3):277-287.
    [26] 张建化, 巩敦卫, 张勇. 动态环境下基于SVR-PSO的多机器人气味源定位方法[J]. 中国科技论文, 2014, 9(1):122-129.
    [27] 李飞, 孟庆浩, 李吉功, 等. 基于P-PSO算法的室内有障碍通风环境下的多机器人气味源搜索[J]. 自动化学报, 2009, 35(12):1573-1579.
    [28] JATMIKO W, SEKIYAMA K, FUKUDA T. A PSO-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment:theory, simulation and measurement[J]. IEEE Computational Intelligence Magazine, 2007, 2(2):37-51.
    [29] GONG D W, ZHANG Y, QI C L. Localising odour source using multi-robot and anemotaxis-based particle swarm optimisation[J]. IET Control Theory and Applications, 2012, 6(11):1661-1670.
    [30] FENG Q L, ZHANG C X, LU J Y, et al. Source localization in dynamic indoor environments with natural ventilation:an experimental study of a particle swarm optimization-based multi-robot olfaction method[J]. Building and Environment, 2019, 161:106228.
    [31] FENG Q L, CAI H, YANG Y B, et al. An Experimental and numerical study on a multi-robot source localization method independent of airflow information in dynamic indoor environments[J]. Sustainable Cities and Society, 2020, 53:101897.
    [32] KAMARUDIN K, SHAKAFF A Y M, BENNETTS V H, et al. Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization[J]. Advanced Robotics, 2018, 32(17):903-917.
    [33] LI J G, MENG Q H, WANG Y, et al. Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm[J]. Autonomous Robots, 2011, 30(3):281-292.
    [34] ISHIDA H, TANAKA H, TANIGUCHI H, et al. Mobile robot navigation using vision and olfaction to search for a gas/odor source[J]. Autonomous Robots, 2006, 20(3):231-238.
    [35] YUAN X L, WANG Y, LIU J X, et al. Experimental and numerical study of airflow distribution optimisation in high-density data centre with flexible baffles[J]. Building and Environment, 2018, 140:128-139,34.
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出版历程
  • 收稿日期:  2020-02-22
  • 网络出版日期:  2021-07-19

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