Online Strategy Synthesis for Safe and Optimized Control of Steerable Needles

Sascha Lehmann
(TUHH)
Antje Rogalla
(TUHH)
Maximilian Neidhardt
(TUHH)
Alexander Schlaefer
(TUHH)
Sibylle Schupp
(TUHH)

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.

In Marie Farrell and Matt Luckcuck: Proceedings Third Workshop on Formal Methods for Autonomous Systems (FMAS 2021), Virtual, 21st-22nd of October 2021, Electronic Proceedings in Theoretical Computer Science 348, pp. 128–135.
Published: 21st October 2021.

ArXived at: http://dx.doi.org/10.4204/EPTCS.348.9 bibtex PDF
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