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Improving the Modularity of AUV Control Systems using Behaviour Trees
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0003-4943-2501
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0001-5656-0259
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
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2018 (English)In: AUV 2018 - 2018 IEEE/OES Autonomous Underwater Vehicle Workshop, Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we show how behaviour trees (BTs) can be used to design modular, versatile, and robust control architectures for mission-critical systems. In particular, we show this in the context of autonomous underwater vehicles (AUVs). Robustness, in terms of system safety, is important since manual recovery of AUVs is often extremely difficult. Further more, versatility is important to be able to execute many different kinds of missions. Finally, modularity is needed to achieve a combination of robustness and versatility, as the complexity of a versatile systems needs to be encapsulated in modules, in order to create a simple overall structure enabling robustness analysis. The proposed design is illustrated using a typical AUV mission.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018.
Keywords [en]
artificial intelligence, autonomous underwater vehicles, behaviour trees, robotic planning, Autonomous vehicles, Forestry, Intelligent robots, Robot programming, Robust control, Robustness (control systems), Control architecture, Mission critical systems, Of autonomous underwater vehicles, Robustness analysis, System safety, Versatile system
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-262469DOI: 10.1109/AUV.2018.8729810ISI: 000492901600108Scopus ID: 2-s2.0-85068333136ISBN: 9781728102535 (print)OAI: oai:DiVA.org:kth-262469DiVA, id: diva2:1361913
Conference
2018 IEEE/OES Autonomous Underwater Vehicle Workshop, AUV 2018, 6 November 2018 through 9 November 2018, Porto, Portugal
Note

QC 20191017

Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2020-01-07Bibliographically approved

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Sprague, ChristopherÖgren, Petter

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