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取得了显著进展，为减轻患者不适提供了有前景的解决方案。然而，大多数机器人柔性内窥镜的自主性有限，导致操作不够直观且具有挑战性，限制了其在临床环境中的应用。尽管已有研究采用管腔跟踪进行自主导航，但这种方法无法适应内窥镜面对结肠壁时存在的障碍物和急转弯情况。本研究提出了一种基于深度强化学习的导航策略，无需依赖管腔跟踪。然而，深度强化学习方法存在安全风险，因其未考虑动作执行过程中的潜在危害。为确保安全性，我们采用约束强化学习方法将策略限制在预定义的安全范围内。此外，我们提出了一种利用形式化验证的模型选择策略，在部署前选择完全安全的策略。我们在虚拟结肠镜环境中验证了该方法，报告显示在300个训练策略中识别出三个完全安全的策略。本研究证明，约束强化学习结合基于形式化验证的模型选择，能够提升手术应用中机器人行为的鲁棒性和安全性。