Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications

Abstract

Detecting chemical compounds using electronic noses is important in many gas sensing related applications. A gas detection system is supposed to indicate a significant event, such as the presence of new chemical compounds or a noteworthy change of concentration levels. Existing gas detection methods typically rely on prior knowledge of target analytes to prepare a dedicated, supervised learning model. However, in some scenarios, such as emergency response, not all the analytes of concern are a priori known and their presence are unlikely to be controlled. In this paper, we take a step towards addressing this issue by proposing an ensemble learning based approach (ELBA) that integrates several one-class classifiers and learns online. The proposed approach is initialized by training several one-class models using clean air only. During the sampling process, the initialized system detects the presence of chemicals, allowing to learn another one-class model and update existing models with self-labelled data. We validated the proposed approach with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an uncontrolled environment. We demonstrated that the ELBA algorithm not only can detect gas exposures but also recognize baseline responses under a suspect short-term sensor drift condition. Depending on the problem setups in practical applications, the present work can be easily hybridized to integrate other supervised learning models when the prior knowledge of target analytes is partially available.

Smell Driven Navigation for Soft Robotic Arms: Artificial Nose and Control

“…Using only two generic gas sensors, they could compare the ratios of the responses to different smells and gases and use such compared measures as an identifier for a specific odour. Later, electronic noses have been applied to navigation of mobile vehicles [16], [17], [18]. No examples exist of application to manipulators.…”

Multitask Deep Learning-Based Pipeline for Gas Leakage Detection via E-Nose and Thermal Imaging Multimodal Fusion

“…Numerous studies have investigated the use of machine/DL methods along with E-noses for gas detection solely [3,[20][21][22][23][24]. However, detection systems based on only gas sensors have some limitations.…”