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Communication-Constrained STL Task Decomposition Through Convex Optimization
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0009-0002-3432-1526
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-3199-4015
University of Southern California, Thomas Lord Department of Computer Science, Los Angeles, CA, USA.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0001-7309-8086
2024 (English)In: 2024 American Control Conference, ACC 2024, Institute of Electrical and Electronics Engineers Inc. , 2024, p. 3517-3523Conference paper, Published paper (Refereed)
Abstract [en]

We propose a method to decompose signal temporal logic tasks for multi-agent systems under communication constraints. Specifically, given a task graph representing task dependencies among couples of agents in the system, we propose to decompose tasks assigned to couples of agents not connected in the communication graph by a set of sub-tasks assigned to couples of communicating agents over the communication graph. To this end, we parameterize the predicates' level set of tasks to be decomposed as hyper-rectangles with parametric centres and dimensions. Convex optimization is then leveraged to find optimal parameters maximising the volume of the predicate's level sets. Moreover, a formal treatment of conflicting conjunctions of formulas in the considered STL fragment is introduced, including sufficient conditions to avoid the insurgence of such conflicts in the final decomposition.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2024. p. 3517-3523
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-354309DOI: 10.23919/ACC60939.2024.10644859Scopus ID: 2-s2.0-85204482477OAI: oai:DiVA.org:kth-354309DiVA, id: diva2:1902968
Conference
2024 American Control Conference, ACC 2024, Toronto, Canada, Jul 10 2024 - Jul 12 2024
Note

QC 20241003

Part of ISBN 979-8-3503-8265-5

Available from: 2024-10-02 Created: 2024-10-02 Last updated: 2024-10-03Bibliographically approved

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Marchesini, GregorioLiu, SiyuanDimarogonas, Dimos V.

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