Tendon-driven continuum robots offer intrinsically safe and contact-rich interactions owing to their kinematic redundancy and structural compliance. However, their perception often depends on external sensors, which increase hardware complexity and limit scalability. This work introduces a unified multi-dynamics modeling framework for tendon-driven continuum robotic systems, exemplified by a spiral-inspired robot named Spirob. The framework integrates motor electrical dynamics, motor-winch dynamics, and continuum robot dynamics into a coherent system model. Within this framework, motor signals such as current and angular displacement are modeled to expose the electromechanical signatures of external interactions, enabling perception grounded in intrinsic dynamics. The model captures and validates key physical behaviors of the real system, including actuation hysteresis and self-contact at motion limits. Building on this foundation, the framework is applied to environmental interaction: first for passive contact detection, verified experimentally against simulation data; then for active contact sensing, where control and perception strategies from simulation are successfully applied to the real robot; and finally for object size estimation, where a policy learned in simulation is directly deployed on hardware. The results demonstrate that the proposed framework provides a physically grounded way to interpret interaction signatures from intrinsic motor signals in tendon-driven continuum robots.

翻译：肌腱驱动连续体机器人凭借其运动学冗余性和结构柔顺性，提供了本质安全且接触丰富的交互能力。然而，其感知通常依赖于外部传感器，这增加了硬件复杂性并限制了可扩展性。本研究提出了一种适用于肌腱驱动连续体机器人系统的统一多动力学建模框架，并以一款受螺旋结构启发的机器人Spirob为例进行阐述。该框架将电机电气动力学、电机-卷筒动力学以及连续体机器人动力学整合为一个连贯的系统模型。在此框架内，电流和角位移等电机信号被建模以揭示外部交互的机电特征，从而实现基于内在动力学的感知。该模型捕捉并验证了真实系统的关键物理行为，包括驱动迟滞和运动极限处的自接触。基于此基础，该框架被应用于环境交互：首先用于被动接触检测，通过实验数据与仿真数据进行验证；其次用于主动接触感知，将仿真中的控制与感知策略成功应用于真实机器人；最后用于物体尺寸估计，将在仿真中学习的策略直接部署于硬件。结果表明，所提出的框架为解释肌腱驱动连续体机器人中来自内在电机信号的交互特征提供了一种基于物理原理的方法。