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Enabling Flow Awareness for Mobile Robots in Partially Observable Environments
Örebro University, School of Science and Technology. (AASS MRO)ORCID iD: 0000-0002-9503-0602
Örebro University, School of Science and Technology. (AASS MRO)ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology. (AASS MRO)ORCID iD: 0000-0002-0804-8637
Örebro University, School of Science and Technology. (AASS MRO)ORCID iD: 0000-0001-5061-5474
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2017 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, 1093-1100 p.Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 2, no 2, 1093-1100 p.
Keyword [en]
Field robots; mapping; social human-robot interaction
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-55174DOI: 10.1109/LRA.2017.2660060ISI: 000413736600094OAI: oai:DiVA.org:oru-55174DiVA: diva2:1070541
Projects
ILIAD
Funder
Knowledge Foundation, 20140220 20130196
Note

Funding Agencies:

EU project SPENCER  ICT-2011-600877 

H2020-ICT project SmokeBot  645101 

H2020-ICT project ILIAD  732737 

Available from: 2017-02-01 Created: 2017-02-01 Last updated: 2017-11-23Bibliographically approved

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

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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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