Time-variant gas distribution mapping with obstacle information

Monroy, Javier; Blanco, Jose-Luis; González-Jiménez, Javier

doi:10.1007/s10514-015-9437-0

Cited by 69 publications

(56 citation statements)

References 28 publications

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“…The source localization estimation has also been addressed using Bayesian methods, using particle filters in outdoor environment [32], [33]; Infotaxis which aim to maximize information gain by reducing the entropy [34]. Gaussian Markov random fields have also been used to address the problem of obstacles in indoor scenarios [35]. Another approach is to formulate the problem as one of regression from sparse measurements.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Active Localization of Gas Leaks Using Fluid Simulation

Asenov

Rutkauskas²,

Reid³

et al. 2019

IEEE Robot. Autom. Lett.

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Sensors are routinely mounted on robots to acquire various forms of measurements in spatio-temporal fields. Locating features within these fields and reconstruction (mapping) of the dense fields can be challenging in resource-constrained situations, such as when trying to locate the source of a gas leak from a small number of measurements. In such cases, a model of the underlying complex dynamics can be exploited to discover informative paths within the field. We use a fluid simulator as a model, to guide inference for the location of a gas leak. We perform localization via minimization of the discrepancy between observed measurements and gas concentrations predicted by the simulator. Our method is able to account for dynamically varying parameters of wind flow (e.g., direction and strength), and its effects on the observed distribution of gas. We develop algorithms for off-line inference as well as for on-line path discovery via active sensing. We demonstrate the efficiency, accuracy and versatility of our algorithm using experiments with a physical robot conducted in outdoor environments. We deploy an unmanned air vehicle (UAV) mounted with a CO2 sensor to automatically seek out a gas cylinder emitting CO2 via a nozzle. We evaluate the accuracy of our algorithm by measuring the error in the inferred location of the nozzle, based on which we show that our proposed approach is competitive with respect to state of the art baselines.

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Section: Related Work and Contributionsmentioning

confidence: 99%

Active Localization of Gas Leaks Using Fluid Simulation

Asenov

Rutkauskas²,

Reid³

et al. 2019

IEEE Robot. Autom. Lett.

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“…The fan consumes 1 W, and the single-board computer consumes around 1.5 W. An alternative to the traditional sensor networks is to employ only one (or just a few) easy-to-transport e-nose. In this case, the mobile enose gathers the volatile chemical information while being transported by a robot [36,37], a vehicle [2,38], or a person [39], for example. Due to the mobility constraint, the e-nose must accomplish the following specifications: (i) be sensitive to gases of interest, (ii) provide georeferenced measurements, (iii) be compact and lightweight, and (iv) either feature a data logging system or offer connectivity to an external device (e.g., computer and smartphone).…”

Section: Configuration For a Monitoring Networkmentioning

confidence: 99%

An Electronic Architecture for Multipurpose Artificial Noses

2018

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This paper deals with the design of an electronic device aimed at the detection and characterization of volatile chemical substances, that is, an electronic nose (e-nose). We pursue the development of a versatile, multipurpose e-nose that can be employed for a wide variety of applications, can integrate heterogeneous sensing technologies, and can offer a mechanism to be customized for different requirements. To that end, we contribute with a fully configurable and decentralized e-nose architecture based on self-contained and intelligent sensor boards (i.e., modules). This design allows for the integration not only of heterogeneous gas sensor technologies, like MOX and AEC sensors, but also of other components, such as GPS or Bluetooth, for a total of up to 127 individual modules. We describe an implementation of a fully operative prototype as an illustrative example of its potential for sensor networks, mobile robotics, and wearable technologies, each using different combinations of sensors.

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“…That is, this configuration provides a real-time gas distribution map [13] that is continuously updated from the gas concentration measurements (Fig. 2c).…”

Section: B Sensory Configurationsmentioning

confidence: 99%

A robotic experiment toward understanding human gas-source localization strategies

Gongora

Monroy

González-Jiménez

2017

2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)

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Abstract-This paper describes an experiment for gas-source localization with a human-teleoperated mobile robot devised to gather data on how humans search for odor-sources. To that end, more than 150 repetitions of the search process are recorded for 69 test subjects, under 4 sensor configurations (including electronic nose, anemometer and video camera) and 4 scenarios (i.e. with different wind-flow conditions and gas-source position). The experiment has been carried out with a ROS-based simulator that allows driving the robot while recording data of interest (e.g. driving commands, robot localization, sensor measurements, groundtruth, etc.) for further analyzing the human process of gas-source searching, and computational fluid dynamics (CFD) to generate realistic and repeatable test conditions. The manuscript describes the different environmental parameters and sensor combinations of the experiment, and explains the methodology under which it was executed. The obtained dataset is publicly available at

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Time-variant gas distribution mapping with obstacle information

Cited by 69 publications

References 28 publications

Active Localization of Gas Leaks Using Fluid Simulation

Active Localization of Gas Leaks Using Fluid Simulation

An Electronic Architecture for Multipurpose Artificial Noses

A robotic experiment toward understanding human gas-source localization strategies

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