A bionic plume tracing method with a mobile robot in outdoor time-varying airflow environment

Zhou, Jieyong; Li, Ji-Gong; Cui, Shigang

doi:10.1109/icinfa.2015.7279679

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…Once contact with the chemical is lost, the robot casts from left to right in a perpendicular direction to the wind in the hopes of encountering another chemical hit. Various adaptations of this technique have been presented over the years (Harvey et al, 2008; Hayes et al, 2002; Ishida et al, 2005; Lilienthal et al, 2003; Waphare et al, 2010; Zhou et al, 2015), which generally vary the method of casting used to re‐encounter the plume. For more detailed explanations of common bioinspired algorithms, the reader is referred to the review paper by Chen and Huang (2019).…”

Section: Gas Source Localization (Gsl)mentioning

confidence: 99%

Gas source localization and mapping with mobile robots: A review

Francis

Griffiths

et al. 2022

Journal of Field Robotics

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Within the last couple of decades, there has been increasing research into the use of mobile robots for gas sensing applications. This has led to many different branches of research, most notably on the topics of gas distribution mapping and source localization. This paper aims to provide an up to date and comprehensive overview of the research on these topics for both controlled and uncontrolled environments with ground‐based or aerial robots. To complement other review papers, a greater focus is placed on recent probabilistic algorithms developed for both single robot and multirobot applications.

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Section: Gas Source Localization (Gsl)mentioning

confidence: 99%

Gas source localization and mapping with mobile robots: A review

Francis

Griffiths

et al. 2022

Journal of Field Robotics

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“…From the formula (1), requiring the distribution of the probability of the odor source, first obtain the turbulent diffusivity D, the literature [17], shows that the turbulent diffusivity D is the diffusion distance x , Reynolds …”

Section: Figure 1: the Probability Distributions Of The Plumementioning

confidence: 99%

Analytical Model of Olfactory Sensory Information in Robot Odor Source Localization

2016

IJSR

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Abstract:The main purpose of establishing the analytical model of olfactory sensory information is to utilize measuring gas concentration of the current moment and wind speed and direction information in recent time to in local area to give odor source's existence measurement (yes, no, or unknown). It is generally known that gas sensor is contact measuring device, which can only measure the gas concentration of its space position, and cannot perceive at a distance. Besides, the spread of the gas mainly relies on environment airflow transport. The environmental airflow motion model should be established firstly so that to quantifiably judge whether there is an odor source in the robot's local territory.

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“…The turbulent diffusion that dominates the dispersion of odor molecules becomes crucial parameter in odor research. Some researchers have investigated the odor characteristics in airflow environment [26]- [28] and in turbulence environment [29], [30]. Most of the gas sensors' works were done in simulation [31]- [33].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Sensing Using Metal Oxide Semiconductor Based-on Support Vector Machine for Odor Classification

Husni¹,

Nurmaini²,

Yani³

et al. 2018

IJECE

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Classifying odor in real experiment presents some challenges, especially the uncertainty of the odor concentration and dispersion that can lead to a difficulty in obtaining an accurate datasets. In this study, to enhance the accuracy, datasets arrangement based on MOS sensors parameters using SVM approach for odor classification is proposed. The sensors are tested to determine the sensors' time response, sensors' peak duration, sensors' sensitivity, and sensors' stability when applied to the various sources at different range. Three sources were used in experimental test, namely: ethanol, methanol, and acetone. The gas sensors characteristics are analyzed in open sampling method to see the sensors' performance in real situation. These performances are considered as the base of choosing the position in collecting the datasets. The sensors in dynamic experiment have average of precision of 93.8-97.0%, the accuracy 93.3-96.7%, and the recall 93.3-96.7%. This values indicates that the collected datasets can support the SVM in improving the intelligent sensing when conducting odor classification work.

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A bionic plume tracing method with a mobile robot in outdoor time-varying airflow environment

Cited by 8 publications

References 14 publications

Gas source localization and mapping with mobile robots: A review

Gas source localization and mapping with mobile robots: A review

Analytical Model of Olfactory Sensory Information in Robot Odor Source Localization

Intelligent Sensing Using Metal Oxide Semiconductor Based-on Support Vector Machine for Odor Classification

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