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Mobile Robots for Localizing Gas Emission Sources on Landfill Sites: Is Bio-Inspiration the Way to Go?

Overview of attention for article published in Frontiers in Neuroengineering, January 2012
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Title
Mobile Robots for Localizing Gas Emission Sources on Landfill Sites: Is Bio-Inspiration the Way to Go?
Published in
Frontiers in Neuroengineering, January 2012
DOI 10.3389/fneng.2011.00020
Pubmed ID
Authors

Victor Hernandez Bennetts, Achim J. Lilienthal, Patrick P. Neumann, Marco Trincavelli

Abstract

Roboticists often take inspiration from animals for designing sensors, actuators, or algorithms that control the behavior of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odor plume. In particular the task of tracking an odor plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behavior of moths, dung beetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro-drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behavior of animals, can be usefully "translated" into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically inspired algorithms.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Italy 1 <1%
Sweden 1 <1%
India 1 <1%
United Kingdom 1 <1%
Canada 1 <1%
Mexico 1 <1%
Denmark 1 <1%
United States 1 <1%
Other 0 0%
Unknown 96 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 27%
Researcher 11 10%
Student > Master 11 10%
Student > Bachelor 9 9%
Student > Doctoral Student 6 6%
Other 17 16%
Unknown 23 22%
Readers by discipline Count As %
Engineering 40 38%
Computer Science 11 10%
Agricultural and Biological Sciences 10 10%
Environmental Science 7 7%
Earth and Planetary Sciences 3 3%
Other 9 9%
Unknown 25 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 January 2012.
All research outputs
#20,165,369
of 22,675,759 outputs
Outputs from Frontiers in Neuroengineering
#70
of 82 outputs
Outputs of similar age
#221,176
of 244,088 outputs
Outputs of similar age from Frontiers in Neuroengineering
#15
of 17 outputs
Altmetric has tracked 22,675,759 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 82 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.