CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊
JST China 收录期刊

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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.
  • 加载中
计量
  • 文章访问数:  138
  • HTML全文浏览量:  11
  • PDF下载量:  1
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-02-22
  • 网络出版日期:  2021-07-19

目录

    /

    返回文章
    返回