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Mobile robots for learning spatio-temporal interpolation models in sensor networks - The Echo State map approach: The Echo State map approach
Örebro University, School of Science and Technology. (AASS Research Centre, Mobile Robotics and Olfaction Lab)ORCID iD: 0000-0003-4026-7490
Örebro University, School of Science and Technology. (AASS Research Centre, Mobile Robotics and Olfaction Lab)ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology. (AASS Research Centre, Mobile Robotics and Olfaction Lab)ORCID iD: 0000-0003-0217-9326
2017 (English)In: 2017 IEEE International Conference on Robotics and Automation (ICRA), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 2659-2665Conference paper, Published paper (Refereed)
Abstract [en]

Sensor networks have limited capabilities to model complex phenomena occuring between sensing nodes. Mobile robots can be used to close this gap and learn local interpolation models. In this paper, we utilize Echo State Networks in order to learn the calibration and interpolation model between sensor nodes using measurements collected by a mobile robot. The use of Echo State Networks allows to deal with temporal dependencies implicitly, while the spatial mapping with a Gaussian Process estimator exploits the fact that Echo State Networks learn linear combinations of complex temporal dynamics. The resulting Echo State Map elegantly combines spatial and temporal cues into a single representation. We showcase the method in the exposure modeling task of building dust distribution maps for foundries, a challenge which is of great interest to occupational health researchers. Results from simulated data and real world experiments highlight the potential of Echo State Maps. While we focus on particulate matter measurements, the method can be applied for any other environmental variables like temperature or gas concentration.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 2659-2665
Keywords [en]
Gaussian processes, learning (artificial intelligence), mobile robots, neurocontrollers, wireless sensor networks, Gaussian process estimator, echo state map approach, gas concentration, mobile robots, particulate matter measurement, sensor networks, spatio-temporal interpolation model learning, temperature concentration, Foundries, Interpolation, Mobile robots, Robot sensing systems, Wireless sensor networks
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-63792DOI: 10.1109/ICRA.2017.7989310Scopus ID: 2-s2.0-85028014826OAI: oai:DiVA.org:oru-63792DiVA, id: diva2:1170470
Conference
2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, Singapore, May 27-June 3, 2017
Projects
RAISE
Funder
Knowledge Foundation, 20130196Available from: 2018-01-03 Created: 2018-01-03 Last updated: 2018-01-09Bibliographically approved

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Schaffernicht, ErikHernandez Bennetts, VictorLilienthal, Achim
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