基于时间加权最大似然估计的室内气体源定位方法  

A gas source localization method in indoor environments based on time weighted maximum likelihood estimation

作　　者：陈泽众 姚逸卿 阳媛 鲍琳欣 Chen Zezhong;Yao Yiqing;Yang Yuan;Bao Linxin(School of Instrument Science&Engineering,Southeast University,Nanjing 210096,China;Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology of Ministry of Education,Nanjing 210096,China)

机构地区：[1]东南大学仪器科学与工程学院,南京210096 [2]微惯性仪表与先进导航技术教育部重点实验室,南京210096

出　　处：《仪器仪表学报》2025年第2期92-102,共11页Chinese Journal of Scientific Instrument

基　　金：2022年度教育部“春晖计划”合作科研项目(HZKY20220128);东南大学“至善青年学者”支持计划项目(2242024RCB0023);江苏省科技厅创新能力建设计划项目(BM2023013-4)资助。

摘　　要：利用移动传感器寻找空气中化学气体的源头可应用于安保搜索、灾区救援和建筑环境领域。探讨了在室内环境中利用移动机器人进行气体源定位的问题,提出了一种基于时间加权的最大似然估计算法TWMLE。该算法基于采样时间加权的机制,通过利用包含气体浓度、风速风向以及自身相对定位的观测样本迭代地估计并趋近局部羽流源位置,有效地应对动态湍流环境中时变的气体和气流分布。同时,基于局部感知窗口方法约束估计位置的可行解空间来保证估计结果可行性,实现在未知环境中对局部羽流的短期估计,有效提升估计稳定性。此外,基于气体检测情况对多次估计结果进行加权平均,有效提升在气体命中时的逆风搜索能力和气味未命中时快速再发现羽流的能力。实验分别在4种具备不同气流条件和障碍物分布的模拟环境以及真实环境中进行,所提出的TWMLE算法在成功率和搜索表现上优于infotaxis算法和surge-cast算法。在实际实验中,TWMLE算法的成功率达到90.0%,高于infotaxis算法的80.0%以及surge-cast算法的60.0%。结果表明,所提出的TWMLE算法在复杂室内环境中能够有效定位气体源。Utilizing mobile sensors to locate chemical gas sources in the air can be applied to security searches,disaster relief,and building environments.This study investigates the problem of gas source localization using mobile robots in indoor environments and proposes a time-weighted maximum likelihood estimation algorithm(TWMLE).Based on a sampling time-weighting mechanism,the algorithm utilizes the observation samples that contain gas concentration,wind speed,direction,and its relative localization to iteratively estimate and approach the position of local plume source,accommodating the time-varying gas distributions and airflows in dynamic turbulent environments.Meanwhile,this study employs a local sensing window to constrain the feasible solution space of the estimated source location to ensure the feasibility of the estimation results,achieving short-term estimation of local plumes in unknown environments and effectively enhancing estimation stability.Additionally,this study weights average the multiple estimation results based on the gas detection condition,effectively enhancing the ability to search upwind when gas is detected and the ability to quickly rediscover the plume when gas is missed.The experiments are implemented to evaluate the proposed method in four indoor environments with different airflow conditions and obstacles,as well as in a real environment.The proposed TWMLE algorithm outperforms both the infotaxis algorithm and the surge-cast algorithm in terms of success rate and search performance.In the real environment,the success rate of the TWMLE algorithm reaches 90.0%,which is higher than the 80.0%of the infotaxis algorithm and the 60.0%of the surge-cast algorithm.The results show that the proposed TWMLE algorithm can effectively locate the gas source in complex indoor environments.

关 键 词：气体源定位 时间加权最大似然估计 室内湍流环境 机器人嗅觉 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP212.9[自动化与计算机技术—计算机科学与技术] TH89[机械工程—仪器科学与技术]