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.

翻译：机器人柔性内窥镜领域已取得显著进展，为减轻患者不适提供了有前景的解决方案。然而，多数机器人柔性内窥镜自主性有限，导致操作缺乏直观性且颇具挑战，限制了其在临床环境中的应用。虽然先前的研究采用管腔追踪实现自主导航，但当内窥镜面临结肠壁时，这些方法无法适应障碍物和急转弯的情况。本研究提出一种基于深度强化学习的导航策略，无需依赖管腔追踪。但深度强化学习方法存在安全风险，因其未考虑所采取动作可能引发的潜在危害。为确保安全，我们采用约束强化学习方法将策略限制在预设的安全范围内。此外，我们提出一种利用形式化验证进行模型选择的策略，在部署前筛选出完全安全的策略。我们在虚拟结肠镜环境中验证该方法，报告称在300个训练策略中识别出三个完全安全的策略。本研究表明，约束强化学习结合形式化验证的模型选择方法，可提升手术应用中机器人行为的鲁棒性与安全性。