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Enhancing Data-Driven Reachability Analysis using Temporal Logic Side Information
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0003-2941-519x
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0001-7309-8086
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2022 (English)In: Proceedings: IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers Inc. , 2022Conference paper, Oral presentation only (Other academic)
Abstract [en]

 This paper presents algorithms for performingdata-driven reachability analysis under temporal logic sideinformation. In certain scenarios, the data-driven reachablesets of a robot can be prohibitively conservative due to theinherent noise in the robot’s historical measurement data. Inthe same scenarios, we often have side information about therobot’s expected motion (e.g., limits on how much a robotcan move in a one-time step) that could be useful for furtherspecifying the reachability analysis. In this work, we showthat if we can model this side information using a signaltemporal logic (STL) fragment, we can constrain the datadriven reachability analysis and safely limit the conservatismof the computed reachable sets. Moreover, we provide formalguarantees that, even after incorporating side information, thecomputed reachable sets still properly over-approximate therobot’s future states. Lastly, we empirically validate the practicality of the over-approximation by computing constrained,data-driven reachable sets for the Small-Vehicles-for-Autonomy(SVEA) hardware platform in two driving scenarios.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2022.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-312118DOI: 10.1109/ICRA46639.2022.9811706Scopus ID: 2-s2.0-85136332386OAI: oai:DiVA.org:kth-312118DiVA, id: diva2:1657578
Conference
39th IEEE International Conference on Robotics and Automation, ICRA 2022, Philadelphia, PA, USA, 23-27 May 2022
Note

QC 20220516

QC 20230626

Available from: 2022-05-11 Created: 2022-05-11 Last updated: 2023-10-26Bibliographically approved

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Alanwar, AmrJiang, FrankSharifi, MaryamDimarogonas, Dimos V.Johansson, Karl H.

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