Using Chemical Sensors as “Noses” for Mobile Robots

Ishida, Hiroshi; Lilienthal, Achim J.; Matsukura, Haruka; Bennetts, Victor Hernandez; Schaffernicht, Erik

doi:10.1002/9781118768495.ch6

Cited by 5 publications

(4 citation statements)

References 75 publications

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“…In order for GSL to be successful, we first need to have a gas sensor that can detect the presence of a target gas in air. We also need an appropriate strategy to decide where the measurements should be made as well as effective algorithms for processing the gas sensor signal and estimating the source location [3]. The focus of this paper is on the processing of signals obtained from gas sensors at multiple locations in a given environment to estimate the location of the gas emission point.…”

Section: Introductionmentioning

confidence: 99%

“…Since molecular diffusion is a slow process, the released gas is spread in the environment mainly by airflow. As the flow we encounter is almost always turbulent, a gas distribution with a complicated and constantly fluctuating shape is generated [3]. GSL algorithms have to be able to cope with the fluctuations caused by turbulence.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, autonomous GSL is useful for tasks that can be too laborious, repetitive, or hazardous for humans to perform [3]. Presently, there are two main methods for autonomous localization: using mobile robots or an array of stationary sensors.…”

Section: Introductionmentioning

confidence: 99%

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Application of Convolutional Long Short-Term Memory Neural Networks to Signals Collected from a Sensor Network for Autonomous Gas Source Localization in Outdoor Environments

Bilgera

Yamamoto

Sawano

et al. 2018

Sensors

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Convolutional Long Short-Term Memory Neural Networks (CNN-LSTM) are a variant of recurrent neural networks (RNN) that can extract spatial features in addition to classifying or making predictions from sequential data. In this paper, we analyzed the use of CNN-LSTM for gas source localization (GSL) in outdoor environments using time series data from a gas sensor network and anemometer. CNN-LSTM is used to estimate the location of a gas source despite the challenges created from inconsistent airflow and gas distribution in outdoor environments. To train CNN-LSTM for GSL, we used temporal data taken from a 5 × 6 metal oxide semiconductor (MOX) gas sensor array, spaced 1.5 m apart, and an anemometer placed in the center of the sensor array in an open area outdoors. The output of the CNN-LSTM is one of thirty cells approximating the location of a gas source. We show that by using CNN-LSTM, we were able to determine the location of a gas source from sequential data. In addition, we compared several artificial neural network (ANN) architectures as well as trained them without wind vector data to estimate the complexity of the task. We found that ANN is a promising prospect for GSL tasks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Convolutional Long Short-Term Memory Neural Networks to Signals Collected from a Sensor Network for Autonomous Gas Source Localization in Outdoor Environments

Bilgera

Yamamoto

Sawano

et al. 2018

Sensors

Self Cite

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“…Since the airflow the robot encounters in gas source localization applications is almost always turbulent, the plume randomly meanders and comes to have a complicated patchy structure (4). Gas source localization in outdoor environments is especially challenging owing to the large fluctuations in the wind direction (5).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Gas Source Location from Fluctuating Readings of Gas Sensors and Anemometer on Mobile Robot in Outdoor Environment

2016

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Here we report on a new method for robotic gas source localization. In the method proposed in this series of work, a mobile robot equipped with gas sensors and an anemometer is used to measure the gas concentration and airflow direction at various locations in the area under consideration. The location of the gas source is estimated by applying a turbulent diffusion model of a gas plume to the sensor data collected by the mobile robot. In the previous version of the method, the value of the turbulent diffusion coefficient in the model needs to be properly adjusted to obtain appropriate estimation results. Therefore, the model is replaced with the one that requires no empirical parameter. The estimation results are presented to show that the proposed method can provide a map showing the area in which a gas source is likely to be found.

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