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Autonomous systems are often applied in uncertain environments, which requireprospective action planning and retrospective data evaluation for futureplanning to ensure safe operation. Formal approaches may support these systemswith safety guarantees, but are usually expensive and do not scale well withgrowing system complexity. In this paper, we introduce online strategysynthesis based on classical strategy synthesis to derive formal safetyguarantees while reacting and adapting to environment changes. To guaranteesafety online, we split the environment into region types which determine theacceptance of action plans and trigger local correcting actions. Using modelchecking on a frequently updated model, we can then derive locally safe actionplans (prospectively), and match the current model against new observations viareachability checks (retrospectively). As use case, we successfully applyonline strategy synthesis to medical needle steering, i.e., navigating a(flexible and beveled) needle through tissue towards a target without damagingits surroundings.

Online Strategy Synthesis for Safe and Optimized Control of Steerable Needles