Odor-source localization in the clean room by an autonomous mobile sensing system

doi:10.1016/0925-4005(96)01907-7

Sensors and Actuators B: Chemical

Volume 33, Issues 1–3, July 1996, Pages 115-121

https://doi.org/10.1016/0925-4005(96)01907-7 Get rights and content

Abstract

Experiments on odor-source localization using an autonomous mobile system were performed in a clean room. A new exploratory algorithm was developed since the algorithm of upwind searching previously developed by our group was found to be applicable only on a limited situation. The switchover of two strategies, moving upwind and along the gas-concentration gradient, was introduced to extend the applicable area. A threshold value of the gas sensor output used for exchanging two strategies was automatically adjusted during localization movements. As the flexibility of the system was enhanced, an ethanol source was successfully localized from almost anywhere in the prepared measurement space.

References (6)

H. Ishida et al.
Study of autonomous mobile sensing system for localization of odor source using gas sensprs and anemometric sensors
Sensors and Actuators A
(1994)
Y. Hiranaka, H. Yamasaki, Measurement of spatial distribution change of gas component by using a sensor array,...
S. Yamakawa et al.
Odor visualization using an optical fiber sensor array

There are more references available in the full text version of this article.

Cited by (112)

3D multi-robot olfaction in naturally ventilated indoor environments: Locating a time-varying source at unknown heights
2024, Science of the Total Environment
Source localization is significant for mitigating indoor air pollution and safeguarding the well-being and safety of occupants. While most study focuses on mechanical ventilation and static sources, this study explores the less-explored domain of locating time-varying sources in naturally ventilated spaces. We have developed an innovative 3D localization system that adjusts to varying heights, significantly enhancing capabilities beyond traditional fixed-height 2D systems. To ensure consistency in experimental conditions, we conducted comparative analyses of 2D and 3D methods, using a swinging fan to simulate natural ventilation. Our findings reveal a substantial disparity in performance: the 2D method had a success rate below 46.7% in cases of height mismatches, while our 3D methods consistently achieved success rates above 66.7%, demonstrating their superior effectiveness in complex environments. Furthermore, we validated the 3D strategies in real naturally ventilated settings, confirming their wider applicability. This research extends the scope of indoor source localization and offers valuable insights and strategies for more effective pollution control.
Regression-enhanced Entrotaxis as an autonomous search algorithm for seeking an unknown gas leakage source
2024, Expert Systems with Applications
Rapid localization of hazardous gas leak sources is a vital task regarding the security of human society. Over the past few decades, autonomous search using mobile sensing devices equipped with gas sensors has become a promising field. Cognitive search strategies, such as Infotaxis and Entrotaxis, have been successfully used to search an unknown source autonomously in turbulence. However, they are time-consuming and may not be efficient because all source parameters (locations and strength) are estimated via particle filtering, especially when the prior information of source strength is rarely known. To address this inefficiency, this paper proposes a hybrid autonomous source searching approach, named as regression-enhanced Entrotaxis, which incorporates the Entrotaxis algorithm with the regression methods. Only the position of leak source is inferred by particle filtering, while the strength is estimated by the least squares based on the historical measurements. Through this way, the searching space in particle filtering is converted from 3-dimensions (source position in $x – y$ plane and source strength) to 2-dimensions (source position in $x – y$ plane), speeding up the computation significantly. Then, the reward function according to maximum entropy sampling principles is utilized to guide the robot’s next move. The performance of the proposed method is compared to that of the Entrotaxis algorithm. The results of Monte Carlo simulations demonstrate that fewer particles will be used to reach a specific success rate, saving time in the search process. Besides, the proposed strategy relies on less prior information regarding source strength, which is more in line with the actual scenario. In addition, the proposed approach is adaptable to a broader search region.
Estimation of activity concentrations of radioactive gases inside containment building of fast breeder reactor FBTR: Leakage of active cover gas
2020, Progress in Nuclear Energy
Citation Excerpt :
Several algorithms to design intelligent mobile robots to locate a gas plume source has been reported in the literature. Most of the strategies employ localization by following the concentration gradient (chemotaxis), and air-flow direction (anemotaxis) (Russell et al., 2013; Li et al., 2011; Ishida et al., 1996, 1998; Marques et al., 2002; Balkovsky and Shraiman, 2002). A detailed review on the strategies employed to localize a gas source and experimental works in this field can be found in [10] and [14].
Leakage of active reactor vessel cover gas through liquid metal seal or various injection and rejection points of the reactor vessel cover gas system, increases radiation levels in the reactor containment building (RCB) of a liquid metal cooled fast reactor (LMFR). Detection of the leakage passage is often difficult. In this article, dispersion of fission product noble gases (FPNGs) and $A r^{41}$ , inside the RCB of fast breeder test reactor (FBTR) at Kalpakkam, has been studied using turbulent diffusion equation for the case of release of active cover gas from a location inside RCB. The resulting estimate of the time-evolution of the gross gamma activity concentration at a detector location, after a controlled release of active cover gas in small quantities from a known point, has been compared against the recorded detector signal. This showed substantial match, with coefficient of determination, $R^{2}$ at 0.84. If there is a steep increase in the activity release rate driven by either an increase in the activity concentration of reactor vessel cover gas (RVCG) due to a clad failure incident or by an increase in the leakage rate of the RVCG due to minor loss of effective isolation of the cover gas region, there will be well discriminated response to it over the prevailing background, in the time series data of gross gamma activity concentration recorded by the gas activity monitors in RCB. This response will be different for different locations of release of active cover gas, making detection of the release site possible, if we know possible release locations, and if the increase in activity release rate is steep enough to allow significant discrimination in response, for different release locations.
An experimental and numerical study on a multi-robot source localization method independent of airflow information in dynamic indoor environments
2020, Sustainable Cities and Society
To locate contaminant sources in dynamic indoor environments, this study presents an improved particle swarm optimization (IPSO) method independent of airflow information and validates the method by combining robot experiments with numerical simulations. The experimental study was first conducted by using three mobile robots to locate an ethanol source in a typical dynamic indoor environment with a fan swinging periodically from left to right. A total of 12 out of 15 experiments were successful, with a success rate of 80%, indicating that the method has a high success rate and strong robustness. Next, the experimental environment was further simulated by CFD, and numerical experiments were conducted. The results show that the success rate and the average number of steps from numerical experiments were consistent with those from robot experiments, indicating the feasibility of using numerical simulations. Finally, the IPSO method was numerically validated and compared with a standard PSO (SPSO) method and a modified PSO method with wind utilization II (WUII) in mixing ventilation (MV) and natural ventilation (NV) cases. With a similar average number of steps, the IPSO method achieved a higher success rate than the comparison methods, indicating the superiority of the IPSO method.
Experimental study on a comprehensive particle swarm optimization method for locating contaminant sources in dynamic indoor environments with mechanical ventilation
2019, Energy and Buildings
Citation Excerpt :
In recent years, multi-robot active olfaction methods have attracted increasing attention due to their high efficiency, good scalability, and strong robustness [27]. To date, most research on robot active olfaction methods [9,28–32] has been conducted in steady-state airflow environments, and only a few attempts have been made in recent years to locate sources in dynamic indoor environments. Compared to steady-state indoor environments, dynamic indoor environments pose two major challenges to robot active olfaction methods.
Source localization is critical to ensuring indoor air quality and environmental safety. Although considerable research has been conducted on source localization in steady-state indoor environments, very few studies have dealt with the more challenging source localization problems in dynamic indoor environments. This paper presents a comprehensive particle swarm optimization (CPSO) method to locate a contaminant source in dynamic indoor environments with mechanical ventilation and develops a multi-robot source localization system to experimentally validate the method. Three robots were used to test the presented method in a typical dynamic indoor environment with periodic swinging of the air supply louvers of a cabinet air conditioner. The presented method was validated with two typical source locations, DS (in the downwind zone) and RS (in the recirculation zone). For DS and RS, 15 and 14 experiments out of 15 experiments were successful, with success rates of 100% and 93.3%, and each robot moved an average of 24.4 and 23.6 steps, respectively. The presented method was also compared with the standard particle swarm optimization (SPSO) and wind utilization II (WUII) methods for locating the source at DS. For the SPSO and WUII methods, only 3 and 6 experiments out of 15 experiments were successful, with success rates of 20% and 40% and averages of 33.0 and 38.0 steps, respectively. The experimental results show that the presented method not only has a much higher success rate than the SPSO and WUII methods but also has higher source localization efficiency.
Multiple odor source localization using diverse-PSO and group-based strategies in an unknown environment
2019, Journal of Computational Science
Citation Excerpt :
The problem of odor source localization can have four different cases. ( 1) Single odor source–single robot [1–6]. ( 2) Single odor source–multiple robot [7–13]. (
This work presents a diverse particle swarm optimization based multi-robot cooperative approach for multiple odor source localization. Major contributions of this paper are with respect to group related tasks and plume following through the introduction of methods for group formation, maintenance of group aggregation, group closeness measurement, group dismantling, and next movement calculation of the robot. Specifically, this paper introduces methods for: (1) localizing multiple odor sources in parallel, (2) maintaining aggregation degree of a group by limiting the maximum number of robots a group can have, (3) measuring the closeness of the formed groups based on which group merging behavior is employed, (4) group dismantling to ensure better resource utilization, and (5) calculating the next move of a robot within the group by diverse- PSO. To bridge the gap between simulation and real-time experiments, sensor odometric error along with localization error in robot positioning is introduced, and the working of the proposed framework is evaluated. Contaminant release is simulated in the 3D indoor environment using Ansys Fluent. Performance of the proposed approach is compared with three state-of-art approaches considering the erroneous and error-free cases. Results validate the effectiveness of the proposed approach.

View all citing articles on Scopus

View full text

Odor-source localization in the clean room by an autonomous mobile sensing system

Abstract

Sensors and Actuators A

Odor visualization using an optical fiber sensor array