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Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO Lab)ORCID-id: 0000-0001-5061-5474
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO Lab)ORCID-id: 0000-0002-9503-0602
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO Lab)ORCID-id: 0000-0002-0804-8637
Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
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2017 (Engelska)Ingår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, nr 2, 1117-1123 s.Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling<?brk?> algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback&ndash;Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 2, nr 2, 1117-1123 s.
Nyckelord [en]
Aerial systems: perception and autonomy, environment monitoring and management, field robots, mapping
Nationell ämneskategori
Datavetenskap (datalogi) Robotteknik och automation
Forskningsämne
Datavetenskap
Identifikatorer
URN: urn:nbn:se:oru:diva-56064DOI: 10.1109/LRA.2017.2661803ISI: 000413736600097OAI: oai:DiVA.org:oru-56064DiVA: diva2:1078590
Forskningsfinansiär
KK-stiftelsen, 20130196
Anmärkning

Funding Agency:

H2020-ICT project SmokeBot  645101

Tillgänglig från: 2017-03-06 Skapad: 2017-03-06 Senast uppdaterad: 2018-01-13Bibliografiskt granskad

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Av författaren/redaktören
Hernandez Bennetts, VictorKucner, Tomasz PiotrSchaffernicht, ErikFan, HanLilienthal, Achim J.
Av organisationen
Institutionen för naturvetenskap och teknik
I samma tidskrift
IEEE Robotics and Automation Letters
Datavetenskap (datalogi)Robotteknik och automation

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