Mobile robot multi-sensor unit for unsupervised gas discrimination in uncontrolled environments

Xing, Yanpeng; Vincent, Timothy A.; Cole, Marina; Gardner, Julian W.; Fan, Han; Bennetts, Victor Hernandez; Schaffernicht, Erik; Lilienthal, Achim J.

doi:10.1109/icsens.2017.8234440

Cited by 19 publications

(16 citation statements)

References 5 publications

(9 reference statements)

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“…When sufficient training data is not available, unsupervised gas discrimination has to be applied. A recent example is in [17], where the authors used a cluster analysis based approach proposed in [18], to perform gas discrimination on the data collected by a mobile robot using an e-nose.…”

Section: Related Workmentioning

confidence: 99%

Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose

Fan

Bennetts

Schaffernicht

et al. 2019

Sensors

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Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response.

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Section: Related Workmentioning

confidence: 99%

Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose

Fan

Bennetts

Schaffernicht

et al. 2019

Sensors

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“…A ground robot equipped with three MOX sensors was driven into plumes of ethanol (95% pure) and 1-propanol (99.5% pure)/acetone (100% pure) at different locations. The MOX sensors used in our experiments are sensitive to these chemical analytes [11]. The gaseous chemicals were released from a beaker filled with liquid of the chemicals, and bubbler was used to facilitate evaporation.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers

Fan

Bennett

Schaffernicht

et al. 2019

2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)

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Detecting chemical compounds using electronic noses is important in many gas sensing related applications. Existing gas detection methods typically use prior knowledge of the target analytes. However, in some scenarios, the analytes to be detected are not fully known in advance, and preparing a dedicated model is not possible. To address this issue, we propose a gas detection approach using an ensemble of oneclass classifiers. The proposed approach is initialized by learning a Mahalanobis-based and a Gaussian based model using clean air only. During the sampling process, the presence of chemicals is detected by the initialized system, which allows to learn a oneclass nearest neighbourhood model without supervision. From then on the gas detection considers the predictions of the three one-class models. The proposed approach is validated with realworld experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an open environment.

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“…The first industrially successful robot, the Unimate, began operation at a General Motors assembly plant in New Jersey, performing automated tasks dangerous to humans 4 . Other examples are mobile robots aiding firefighters in environments with restricted visibility 8 , robots mimicking hand and arm movements conducted by a human at a safe distance to reduce health risks for vulnerable workers, for example, in nuclear environments 9 , and robotic aid in mountain rescue 10 . The current COVID-19 pandemic brings along several threats and restrictions to our society.…”

Section: Robotic Technology In the Age Of Pandemicsmentioning

confidence: 99%