  The field of robotic Flexible Endoscopes (FEs) has progressed significantly,
offering a promising solution to reduce patient discomfort. However, the
limited autonomy of most robotic FEs results in non-intuitive and challenging
manoeuvres, constraining their application in clinical settings. While previous
studies have employed lumen tracking for autonomous navigation, they fail to
adapt to the presence of obstructions and sharp turns when the endoscope faces
the colon wall. In this work, we propose a Deep Reinforcement Learning
(DRL)-based navigation strategy that eliminates the need for lumen tracking.
However, the use of DRL methods poses safety risks as they do not account for
potential hazards associated with the actions taken. To ensure safety, we
exploit a Constrained Reinforcement Learning (CRL) method to restrict the
policy in a predefined safety regime. Moreover, we present a model selection
strategy that utilises Formal Verification (FV) to choose a policy that is
entirely safe before deployment. We validate our approach in a virtual
colonoscopy environment and report that out of the 300 trained policies, we
could identify three policies that are entirely safe. Our work demonstrates
that CRL, combined with model selection through FV, can improve the robustness
and safety of robotic behaviour in surgical applications.
