This paper presents a shape-aware whole-body control framework for tendon-driven continuum robots with direct application to endoluminal surgical navigation. Endoluminal procedures, such as bronchoscopy, demand precise and safe navigation through tortuous, patient-specific anatomy where conventional tip-only control often leads to wall contact, tissue trauma, or failure to reach distal targets. To address these challenges, our approach combines a physics-informed backbone model with residual learning through an Augmented Neural ODE, enabling accurate shape estimation and efficient Jacobian computation. A sampling-based Model Predictive Path Integral (MPPI) controller leverages this representation to jointly optimize tip tracking, backbone conformance, and obstacle avoidance under actuation constraints. A task manager further enhances adaptability by allowing real-time adjustment of objectives, such as wall clearance or direct advancement, during tele-operation. Extensive simulation studies demonstrate millimeter-level accuracy across diverse scenarios, including trajectory tracking, dynamic obstacle avoidance, and shape-constrained reaching. Real-robot experiments on a bronchoscopy phantom validate the framework, showing improved lumen-following accuracy, reduced wall contacts, and enhanced adaptability compared to joystick-only navigation and existing baselines. These results highlight the potential of the proposed framework to increase safety, reliability, and operator efficiency in minimally invasive endoluminal surgery, with broader applicability to other confined and safety-critical environments.

翻译：本文提出了一种用于肌腱驱动连续体机器人的形状感知全身控制框架，可直接应用于腔内手术导航。支气管镜检查等腔内手术需要在曲折、患者特定的解剖结构中实现精确且安全的导航，而传统的仅尖端控制方法常导致管壁接触、组织损伤或无法到达远端目标。为解决这些挑战，我们的方法将物理信息骨架模型与通过增强神经常微分方程实现的残差学习相结合，从而实现精确的形状估计和高效的雅可比矩阵计算。基于采样的模型预测路径积分控制器利用该表示，在驱动约束下联合优化尖端跟踪、骨架顺应性和避障。任务管理器通过允许在遥操作期间实时调整目标（如管壁间隙或直接推进），进一步增强了适应性。广泛的仿真研究表明，该框架在多种场景下（包括轨迹跟踪、动态避障和形状约束到达）均实现了毫米级精度。在支气管镜体模上的真实机器人实验验证了该框架，与仅使用操纵杆导航和现有基线方法相比，显示出更高的管腔跟随精度、更少的管壁接触以及更强的适应性。这些结果突显了所提框架在提高微创腔内手术的安全性、可靠性和操作效率方面的潜力，并适用于其他受限和安全关键环境。