Olfactory coordinated area coverage

Larionova, Svetlana; Almeida, Nuno; Marques, Lino; Almeida, A.T. de

doi:10.1007/s10514-006-7099-7

Cited by 23 publications

(17 citation statements)

References 9 publications

(8 reference statements)

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“…Most of the gas sensing robots reported so far are equipped with commercial metal-oxide gas sensors because of their reasonably high sensitivity and fair response time (typically <5 s) [17][18][19][20][21][22][23][24][25][26][27][28][29]. They respond to flammable gas at a sub-ppm level.…”

Section: Sensors For Gas Detectionmentioning

confidence: 99%

“…[24], the recovery time of a metal-oxide gas sensor on the robot was reduced to 1 s by selecting a sensor with fast response and using it with a suction pump to quickly replace air samples around the sensor. Mobile robots equipped with an electronic nose system can detect a specific target gas even under the presence of other interfering gases [8,11,17,19,27,28,[38][39][40][41]. An electronic nose (or e-nose in short) consists of an array of gas sensors and a pattern classifier.…”

Section: Sensors For Gas Detectionmentioning

confidence: 99%

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Using Chemical Sensors as “Noses” for Mobile Robots

Ishida

Lilienthal

Matsukura

et al. 2016

Essentials of Machine Olfaction and Taste

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Section: Sensors For Gas Detectionmentioning

confidence: 99%

Section: Sensors For Gas Detectionmentioning

confidence: 99%

Using Chemical Sensors as “Noses” for Mobile Robots

Ishida

Lilienthal

Matsukura

et al. 2016

Essentials of Machine Olfaction and Taste

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“…This device draws air from the floor to the sensor inlet and blows air in the opposite direction around the sensor inlet in order to create an "air curtain". However, the efficacy of this device has been questioned by Larionova et al in [71], and it is currently unclear whether the different results have been obtained because of small differences in the implementation of the air curtain or to differences in the tasks considered (detecting a narrow trail in [69] versus a comparatively wide area in [71]). …”

Section: Chemical Trail Followingmentioning

confidence: 99%

“…Most of the works assume the presence of two gas sensors which sample the analyte in the proximity of the ground [70,72,73]. In [71] a strategy based on Figure 4.14: Device for creating an air curtain. The flux of fresh air is represented by blue lines, while the flux of air to be analyzed by the gas sensor is represented by red lines.…”

Section: Chemical Trail Followingmentioning

confidence: 99%

Gas Discrimination for Mobile Robots

Trincavelli

2011

Künstl Intell

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The problem addressed in this thesis is discrimination of gases with an array of partially selective gas sensors. Metal oxide gas sensors are the most common gas sensing technology since they have, compared to other gas sensing technologies, a high sensitivity to the target compounds, a fast response time, they show a good stability of the response over time and they are commercially available. One of the most severe limitation of metal oxide gas sensors is the scarce selectivity, that means that they do not respond only to the compound for which they are optimized but also to other compounds. One way to enhance the selectivity of metal oxide gas sensors is to build an array of sensors with different, and partially overlapping, selectivities and then analyze the response of the array with a pattern recognition algorithm. The concept of an array of partially selective gas sensors used together with a pattern recognition algorithm is known as an electronic nose (e-nose).In this thesis the attention is focused on e-nose applications related mobile robotics. A mobile robot equipped with an e-nose can address tasks like environmental monitoring, search and rescue operations or exploration of hazardous areas. In e-noses mounted on mobile robots the sensing array is most often directly exposed to the environment without the use of a sensing chamber. This choice is often made because of constraints in weight, costs and because the dynamic response obtained by the direct interaction of the sensors with the gas plume contains valuable information. However, this setup introduces additional challenges due to the gas dispersion that characterize natural environments. Turbulent and chaotic gas dispersal causes the array of sensors to be exposed to rapid changes in concentration that cause the sensor response to be highly dynamic and to seldom reach a steady state. Therefore the discrimination of gases has to be performed on features extracted from the dynamics of the signal. The problem is further complicated by variations in temperature and humidity, physical variables to which metal oxide gas sensors are crossensitive. For these reasons the problem of discrimination of gases when an array of sensors is directly exposed to the environment is different from when the array of sensors is in a controlled chamber.i ii This thesis is a compilation of papers whose contributions are two folded. On one side new algorithms for discrimination of gases with an array of sensors directly exposed to the environment are presented. On the other side, innovative experimental setups are proposed. These experimental setups enable the collection of high quality data that allow a better insight in the problem of discrimination of gases with mobile robots equipped with an e-nose. The algorithmic contributions start with the design and validation of a gas discrimination algorithm for gas sensors array directly exposed to the environment. The algorithm is then further developed in order to be able to run online on a robot, thereby enabling t...

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“…Some reference works in this area are: the collaborative spiral surge algorithm proposed by Hayes and co-workers for finding odor sources with a group of robots [6]; the cooperative area coverage using olfaction that was addressed by [7] proposing an online complete coverage algorithm based on the utilization of chemical markings and a biologically-inspired algorithm for gas/odor source localization in an indoor environment with no strong airflow Institute of Systems and Robotics, University of Coimbra, Portugal{ali, jgnunes, pvsousa, rfaria, lino}@isr.uc.pt by [8]. The problem of finding traces of odor plumes in large search spaces has been addressed in [9] using a group of mobile robots coordinated by a particle swarm-based algorithm (PSO).…”

Section: Introductionmentioning

confidence: 99%

An olfactory-based robot swarm navigation method

Marjovi

Nunes

Sousa

et al. 2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-This paper presents a novel robot swarming navigation algorithm in order to find the odor sources in an unknown environment, based on the ability of each swarm member to sense the odor. Each robot in the swarm has a cooperative localization system which uses wireless network as a mean of measuring the distance from the other robots. In this method, at least three robots act as stationary measurement beacons while the other robots of the swarm navigate in the environment towards the odor source. In the next step, the roles of the robots will be switched and some other robots will act as beacons. The experimental tests report a good result in finding the odor source and also the accuracy of localization system 1 .

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Olfactory coordinated area coverage

Cited by 23 publications

References 9 publications

Using Chemical Sensors as “Noses” for Mobile Robots

Using Chemical Sensors as “Noses” for Mobile Robots

Gas Discrimination for Mobile Robots

An olfactory-based robot swarm navigation method

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