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Trajectory generation for networked UAVs using online learning for delay compensation
KTH, School of Electrical Engineering and Computer Science (EECS).
Seoul Natl Univ, Dept Mech & Aerosp Engn, Seoul, South Korea..
Seoul Natl Univ, Dept Mech & Aerosp Engn, Seoul, South Korea..
KTH, School of Electrical Engineering and Computer Science (EECS).ORCID iD: 0000-0001-9940-5929
2017 (English)In: 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017, IEEE, 2017, p. 1941-1946Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a trajectory generation mechanism based on machine learning for a network of unmanned aerial vehicles (UAVs). For delay compensation, we apply an online regression technique to learn a pattern of network-induced effects on UAV maneuvers. Due to online learning, the control system not only adapts to changes to the environment, but also maintains a fixed amount of training data. The proposed algorithm is evaluated on a collaborative trajectory tracking task for two UAVs. Improved tracking is achieved in comparison to a conventional linear compensation algorithm.

Place, publisher, year, edition, pages
IEEE, 2017. p. 1941-1946
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-228178DOI: 10.1109/CCTA.2017.8062740ISI: 000426981500310Scopus ID: 2-s2.0-85047635778ISBN: 9781509021826 (print)OAI: oai:DiVA.org:kth-228178DiVA, id: diva2:1209009
Conference
1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017, Mauna Lani Bay HotelKohala Coast, United States, 27 August 2017 through 30 August 2017
Note

QC 20180521

Available from: 2018-05-21 Created: 2018-05-21 Last updated: 2018-06-12Bibliographically approved

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Authority records BETA

Yoo, JaehyunJohansson, Karl H.

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  • apa
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  • ieee
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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