Towards Odor-Sensitive Mobile Robots

Monroy, Javier; González-Jiménez, Javier

doi:10.4018/978-1-5225-8060-7.ch070

Cited by 7 publications

(4 citation statements)

References 80 publications

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“…Robotic olfaction (RO) is a recent filed of research that involves many important technological and scientific challenges [24][25][26][27][28][29]. It refers to the use of mobile robots in combination with a sensing technology to perceive gas release in the environment [30].…”

Section: Robot Olfactory Problemmentioning

confidence: 99%

“…A general and powerful extension in this research field suggested in different studies as a potential future development, is the adaptation of methodologies for the third spatial dimension and for augmenting the solution to a multirobot collaboration scheme. These improvements in many cases may help to reduce the time needed to cover a large area and improve at the same time the quality of the sampled data when a low-cost multisensory system is used [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38].…”

Section: Drones In the Gsl Applicationmentioning

confidence: 99%

See 1 more Smart Citation

Drones and Sensors Ecosystem to Maximise the “Storm Effects” in Case of CBRNe Dispersion in Large Geographic Areas

Martellucci¹,

Chierici²,

Giovanni³

et al. 2021

IJSSE

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The advancements in the field of robotics, specifically in the aerial robotics, combined with technological improvements of the capability of drones, have increased dramatically the use of these devices as a valuable tool in a wide range of applications. From civil to commercial and military area, the requirements in the emerging application for monitoring complex scenarios that are potentially dangerous for operators give rise to the need of a more powerful and sophisticated approach. This work aims at proposing the use of swarm drones to increase plume detection, tracking and source declaration for chemical releases. The several advantages which this technology may lead to this research and application fields are investigated, as well as the research and technological activities to be performed to make swarm drones efficient, reliable, and accurate.

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Section: Robot Olfactory Problemmentioning

confidence: 99%

Section: Drones In the Gsl Applicationmentioning

confidence: 99%

Drones and Sensors Ecosystem to Maximise the “Storm Effects” in Case of CBRNe Dispersion in Large Geographic Areas

Martellucci¹,

Chierici²,

Giovanni³

et al. 2021

IJSSE

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“…CBRN related robotics has seen a swell of research interest in recent years, as recently summarised in Monroy and Gonzalez-Jimenez [2019], due to the ever increasing computational capabilities of small onboard chipsets and chemical sensors that can be easily fitted to mobile platforms including small UAVs. Coupled with their mobility to collect large amounts of data at any location, these systems are highly beneficial compared to the traditional approach of sparse static sensors running alongside complex CFD models that can take several days to resolve.…”

Section: State Of the Artmentioning

confidence: 99%

Autonomous search of an airborne release in urban environments using informed tree planning

Rhodes,

Liu,

Westoby

et al. 2021

Preprint

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The use of autonomous vehicles for chemical source localisation is a key enabling tool for disaster response teams to safely and efficiently deal with chemical emergencies. Whilst much work has been performed on source localisation using autonomous systems, most previous works have assumed an open environment or employed simplistic obstacle avoidance, separate to the estimation procedure. In this paper, we explore the coupling of the path planning task for both source term estimation and obstacle avoidance in a holistic framework. The proposed system intelligently produces potential gas sampling locations based on the current estimation of the wind field and the local map. Then a tree search is performed to generate paths toward the estimated source location that traverse around any obstacles and still allow for exploration of potentially superior sampling locations. The proposed informed tree planning algorithm is then tested against the Entrotaxis technique in a series of high fidelity simulations. The proposed system is found to reduce source position error far more efficiently than Entrotaxis in a feature rich environment, whilst also exhibiting vastly more consistent and robust results.Keywords source term estimation • path planning • environmental sampling • autonomous search • informed tree

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“…Portable, low-cost electronic noses (e-noses) based on an array of partially selective MOX sensors are widely used for gas sensing in many applications, including but not limited to landfill ( Perera et al, 2001 ; De Vito et al, 2011 ; Gębicki et al, 2014 ) and ship emission monitoring ( Yuan et al, 2020 ), chemical leakage detection in industrial sites ( Capelli et al, 2014 ; Bourne et al, 2020 ), early fire detection ( Young et al, 2003 ; Scorsone et al, 2006 ; Joseph et al, 2015 ), exploration of interested areas for emergency response or environmental monitoring ( He et al, 2019 ; Anyfantis et al, 2021 ), etc ( Monroy and Gonzalez-Jimenez, 2019 ; Burgués and Marco, 2020a ). In many real-world gas sensing related applications, gas detection comes as a fundamental task that recognizes the presence of gases by monitoring their concentration levels exceeding pre-defined thresholds.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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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.

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Towards Odor-Sensitive Mobile Robots

Cited by 7 publications

References 80 publications

Drones and Sensors Ecosystem to Maximise the “Storm Effects” in Case of CBRNe Dispersion in Large Geographic Areas

Drones and Sensors Ecosystem to Maximise the “Storm Effects” in Case of CBRNe Dispersion in Large Geographic Areas

Autonomous search of an airborne release in urban environments using informed tree planning

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

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