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From Insects to Micro Air Vehicles: A Comparison of Reactive Plume Tracking Strategies
BAM Federal Institute for Materials Research and Testing, Berlin, Germany. (AASS)ORCID iD: 0000-0003-0217-9326
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology.ORCID iD: 0000-0003-0217-9326
BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
2016 (English)In: Intelligent Autonomous Systems 13, Springer, 2016, 1533-1548 p.Conference paper, Published paper (Refereed)
Resource type
Text
Abstract [en]

Insect behavior is a common source of inspiration for roboticists and computer scientists when designing gas-sensitive mobile robots. More specifically, tracking airborne odor plumes, and localization of distant gas sources are abilities that suit practical applications such as leak localization and emission monitoring. Gas sensing with mobile robots has been mostly addressed with ground-based platforms and under simplified conditions and thus, there exist a significant gap between the outstanding insect abilities and state-of-the-art robotics systems. As a step toward practical applications, we evaluated the performance of three biologically inspired plume tracking algorithms. The evaluation is carried out not only with computer simulations, but also with real-world experiments in which, a quadrocopter-based micro Unmanned Aerial Vehicle autonomously follows a methane trail toward the emitting source. Compared to ground robots, micro UAVs bring several advantages such as their superior steering capabilities and fewer mobility restrictions in complex terrains. The experimental evaluation shows that, under certain environmental conditions, insect like behavior in gas-sensitive UAVs is feasible in real-world environments.

Place, publisher, year, edition, pages
Springer, 2016. 1533-1548 p.
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 302
Keyword [en]
Autonomous micro UAV, Mobile robot olfaction, Gas source localization, Reactive plume tracking, Biologically inspired robots
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-51408DOI: 10.1007/978-3-319-08338-4_110ISI: 000377956900110Scopus ID: 2-s2.0-84945936654ISBN: 978-3-319-08338-4 (print)ISBN: 978-3-319-08337-7 (print)OAI: oai:DiVA.org:oru-51408DiVA: diva2:950089
Conference
13th International Conference on Intelligent Autonomous Systems (IAS), Centro Congressi Padova, Padova, Italy, July 15-18, 2014
Available from: 2016-07-27 Created: 2016-07-19 Last updated: 2017-10-17Bibliographically approved

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