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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 universitet, Institutionen för naturvetenskap och teknik. (AASS Research Centre, Mobile Robotics and Olfaction Lab)ORCID-id: 0000-0003-4026-7490
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS Research Centre, Mobile Robotics and Olfaction Lab)ORCID-id: 0000-0001-5061-5474
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS Research Centre, Mobile Robotics and Olfaction Lab)ORCID-id: 0000-0003-0217-9326
2017 (engelsk)Inngår i: 2017 IEEE International Conference on Robotics and Automation (ICRA), Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 2659-2665Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2017. s. 2659-2665
Emneord [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
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
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
Konferanse
2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, Singapore, May 27-June 3, 2017
Prosjekter
RAISE
Forskningsfinansiär
Knowledge Foundation, 20130196Tilgjengelig fra: 2018-01-03 Laget: 2018-01-03 Sist oppdatert: 2018-01-09bibliografisk kontrollert

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Schaffernicht-ICRA2017-EchoStateMaps.pdf(2512 kB)214 nedlastinger
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Schaffernicht, ErikHernandez Bennetts, VictorLilienthal, Achim

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