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Online strategy synthesis for safe and optimized control of steerable needles
Citation Link: https://doi.org/10.15480/882.3866
Publikationstyp
Journal Article
Publikationsdatum
2021-10-25
Sprache
English
Author
First published in
Number in series
348
Start Page
128
End Page
135
Citation
Electronic proceedings in theoretical computer science 348: 128-135 (2021)
Contribution to Conference
Publisher DOI
Scopus ID
ArXiv ID
Publisher
NICTA
Autonomous systems are often applied in uncertain environments, which require prospective action planning and retrospective data evaluation for future planning to ensure safe operation. Formal approaches may support these systems with safety guarantees, but are usually expensive and do not scale well with growing system complexity. In this paper, we introduce online strategy synthesis based on classical strategy synthesis to derive formal safety guarantees while reacting and adapting to environment changes. To guarantee safety online, we split the environment into region types which determine the acceptance of action plans and trigger local correcting actions. Using model checking on a frequently updated model, we can then derive locally safe action plans (prospectively), and match the current model against new observations via reachability checks (retrospectively). As use case, we successfully apply online strategy synthesis to medical needle steering, i.e., navigating a (flexible and beveled) needle through tissue towards a target without damaging its surroundings.
DDC Class
004: Informatik
600: Technik
610: Medizin
620: Ingenieurwissenschaften
Funding Organisations
More Funding Information
This study was partially funded by the TUHH i3 lab initiative (T-LP-E01-WTM-1801-02), DFG SCHU 2479, and DFG
SCHL 1844/6-1.
SCHL 1844/6-1.
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