A Unified Multi-Dynamics Framework for Perception-Oriented Modeling in Tendon-Driven Continuum Robots

2025-11-22

AI Summary

Tendon-driven continuum robots suffer from hardware complexity and poor scalability due to reliance on external sensors. This paper proposes a unified multi-domain dynamic modeling paradigm that, for the first time, deeply couples motor electrical dynamics, winch dynamics, and robot body mechanical dynamics—enabling environment perception solely from intrinsic electrical signals (motor current) and joint angular displacement. The approach supports passive contact detection, active tactile sensing, and object dimension estimation without auxiliary sensing hardware, while ensuring direct policy transfer from simulation to physical deployment. Experimental validation on the helical-structure robot Spirob demonstrates high contact detection accuracy, effective self-contact identification, and low dimensional estimation error. The model exhibits both physical consistency and cross-platform generalizability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Low Level & Physics-based Vision
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• Developing intrinsic perception without external sensors for tendon-driven robots
• Modeling multi-dynamics to capture electromechanical signatures of interactions
• Enabling simulation-to-reality transfer for contact detection and object estimation

Innovation

• Integrates motor electrical dynamics with continuum robot dynamics
• Models motor signals to expose electromechanical interaction signatures
• Enables simulation-to-reality transfer for perception and control