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Enabling Flow Awareness for Mobile Robots in Partially Observable Environments
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO)ORCID-id: 0000-0002-9503-0602
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO)ORCID-id: 0000-0001-8658-2985
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO)ORCID-id: 0000-0002-0804-8637
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO)ORCID-id: 0000-0001-5061-5474
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2017 (engelsk)Inngår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, nr 2, s. 1093-1100Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Understanding the environment is a key requirement for any autonomous robot operation. There is extensive research on mapping geometric structure and perceiving objects. However, the environment is also defined by the movement patterns in it. Information about human motion patterns can, e.g., lead to safer and socially more acceptable robot trajectories. Airflow pattern information allow to plan energy efficient paths for flying robots and improve gas distribution mapping. However, modelling the motion of objects (e.g., people) and flow of continuous media (e.g., air) is a challenging task. We present a probabilistic approach for general flow mapping, which can readily handle both of these examples. Moreover, we present and compare two data imputation methods allowing to build dense maps from sparsely distributed measurements. The methods are evaluated using two different data sets: one with pedestrian data and one with wind measurements. Our results show that it is possible to accurately represent multimodal, turbulent flow using a set of Gaussian Mixture Models, and also to reconstruct a dense representation based on sparsely distributed locations.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 2, nr 2, s. 1093-1100
Emneord [en]
Field robots; mapping; social human-robot interaction
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
URN: urn:nbn:se:oru:diva-55174DOI: 10.1109/LRA.2017.2660060ISI: 000413736600094OAI: oai:DiVA.org:oru-55174DiVA, id: diva2:1070541
Prosjekter
ILIAD
Forskningsfinansiär
Knowledge Foundation, 20140220 20130196
Merknad

Funding Agencies:

EU project SPENCER  ICT-2011-600877 

H2020-ICT project SmokeBot  645101 

H2020-ICT project ILIAD  732737 

Tilgjengelig fra: 2017-02-01 Laget: 2017-02-01 Sist oppdatert: 2017-11-23bibliografisk kontrollert

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Kucner, Tomasz PiotrMagnusson, MartinSchaffernicht, ErikHernandez Bennetts, Victor ManuelLilienthal, Achim

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IEEE Robotics and Automation Letters

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