用强化学习让柔性内窥镜靠接触壁面导航，提升精准度和稳定性。
Contact-Aided Navigation of Flexible Robotic Endoscope Using Deep Reinforcement Learning in Dynamic Stomach
- 通过深度强化学习利用内窥镜与胃壁的接触力反馈来导航
- 在动态胃中实现100%成功率，平均误差仅1.6毫米
- 适合需要高精度内窥镜导航的手术机器人研究者
在胃肠道内导航柔性机器人内窥镜（FRE）对诊断和治疗至关重要。然而，在动态胃中导航尤为困难，因为FRE必须学会有效利用与可变形胃壁的接触以到达目标位置。为此，我们提出一种基于深度强化学习（DRL）的接触辅助导航（CAN）策略，利用接触力反馈提升运动稳定性和导航精度。训练环境采用基于物理的有限元方法（FEM）模拟可变形胃。使用近端策略优化（PPO）算法训练后，该方法在静态和动态胃环境中均达到100%成功率，端点与目标间平均误差为1.6毫米；在具有更强外部干扰的未知挑战场景中仍保持85%成功率。结果表明，基于DRL的CAN策略显著优于以往方法，大幅提升了FRE导航性能。
原文摘要 · Abstract (English)
Navigating a flexible robotic endoscope (FRE) through the gastrointestinal tract is critical for surgical diagnosis and treatment. However, navigation in the dynamic stomach is particularly challenging because the FRE must learn to effectively use contact with the deformable stomach walls to reach target locations. To address this, we introduce a deep reinforcement learning (DRL) based Contact-Aided Navigation (CAN) strategy for FREs, leveraging contact force feedback to enhance motion stability and navigation precision. The training environment is established using a physics-based finite element method (FEM) simulation of a deformable stomach. Trained with the Proximal Policy Optimization (PPO) algorithm, our approach achieves high navigation success rates (within 3 mm error between the FRE's end-effector and target) and significantly outperforms baseline policies. In both static and dynamic stomach environments, the CAN agent achieved a 100% success rate with 1.6 mm average error, and it maintained an 85% success rate in challenging unseen scenarios with stronger external disturbances. These results validate that the DRL-based CAN strategy substantially enhances FRE navigation performance over prior methods.
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