In this paper, we present a novel approach to navigating endoluminal channels, specifically within the bronchial tubes, using Q-learning, a reinforcement learning algorithm. The proposed method involves training a Q-learning agent to navigate a simulated environment resembling bronchial tubes, with the ultimate goal of enabling the navigation of real bronchial tubes. We discuss the formulation of the problem, the simulation environment, the Q-learning algorithm, and the results of our experiments. Our results demonstrate the agent's ability to learn effective navigation strategies and reach predetermined goals within the simulated environment. This research contributes to the development of autonomous robotic systems for medical applications, particularly in challenging anatomical environments.

翻译：本文提出了一种利用强化学习算法Q学习进行腔内通道（特别是支气管）导航的新方法。该方法训练一个Q学习代理在模拟支气管环境中导航，最终目标是实现真实支气管的导航。我们讨论了问题建模、仿真环境、Q学习算法以及实验结果。结果表明，代理能够学习有效的导航策略，并在模拟环境中到达预定目标。本研究为医疗应用中的自主机器人系统开发做出了贡献，特别是在具有挑战性的解剖学环境中。