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Enabling Flow Awareness for Mobile Robots in Partially Observable Environments
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO)ORCID iD: 0000-0002-9503-0602
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO)ORCID iD: 0000-0001-8658-2985
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO)ORCID iD: 0000-0002-0804-8637
Örebro University, School of Science and Technology, Örebro University, Sweden. (AASS MRO)ORCID iD: 0000-0001-5061-5474
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2017 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766Article in journal (Refereed) Epub ahead of print
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.
Keyword [en]
Trajectory, Robots, Atmospheric modeling, Planning, Gaussian distribution, Probabilistic logic, Gaussian mixture model
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-55174DOI: 10.1109/LRA.2017.2660060OAI: oai:DiVA.org:oru-55174DiVA: diva2:1070541
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ILIAD
Available from: 2017-02-01 Created: 2017-02-01 Last updated: 2017-02-01Bibliographically approved

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Kucner, Tomasz PiotrMagnusson, MartinSchaffernicht, ErikHernandez Bennetts, Victor ManuelLilienthal, Achim
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School of Science and Technology, Örebro University, Sweden
Robotics

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