Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Cable-driven Continuum Robotics: Proprioception via Proximal-integrated Force Sensing

Gang Zhang, Junyan Yan, Jibiao Chen, Shing Shin Cheng

Abstract

Micro-scale continuum robots face significant limitations in achieving three-dimensional contact force perception, primarily due to structural miniaturization, nonlinear mechanical, and sensor integration. To overcome these limitations, this paper introduces a novel proprioception method for cable-driven continuum robots based on proximal-integrated force sensing (i.e., cable tension and six-axis force/torque (F/T) sensor), inspired by the tendon-joint collaborative sensing mechanism of the finger. By integrating biomechanically inspired design principles with nonlinear modeling, the proposed method addresses the challenge of force perception (including the three-dimensional contact force and the location of the contact point) and shape estimation in micro-scale continuum robots. First, a quasi-bionic mapping between human tissues/organs and robot components is established, enabling the transfer of the integrated sensing strategy of tendons, joints, and neural feedback to the robotic system. Second, a multimodal perception strategy is developed based on the structural constraints inherent to continuum robots. The complex relationships among mechanical and material nonlinearities, robot motion states, and contact forces are formulated as an optimization problem to reduce the perception complexity. Finally, experimental validation demonstrates the effectiveness of the proposed method. This work lays the foundation for developing safer and smarter continuum robots, enabling broader clinical adoption in complex environments.

Cable-driven Continuum Robotics: Proprioception via Proximal-integrated Force Sensing

Abstract

Micro-scale continuum robots face significant limitations in achieving three-dimensional contact force perception, primarily due to structural miniaturization, nonlinear mechanical, and sensor integration. To overcome these limitations, this paper introduces a novel proprioception method for cable-driven continuum robots based on proximal-integrated force sensing (i.e., cable tension and six-axis force/torque (F/T) sensor), inspired by the tendon-joint collaborative sensing mechanism of the finger. By integrating biomechanically inspired design principles with nonlinear modeling, the proposed method addresses the challenge of force perception (including the three-dimensional contact force and the location of the contact point) and shape estimation in micro-scale continuum robots. First, a quasi-bionic mapping between human tissues/organs and robot components is established, enabling the transfer of the integrated sensing strategy of tendons, joints, and neural feedback to the robotic system. Second, a multimodal perception strategy is developed based on the structural constraints inherent to continuum robots. The complex relationships among mechanical and material nonlinearities, robot motion states, and contact forces are formulated as an optimization problem to reduce the perception complexity. Finally, experimental validation demonstrates the effectiveness of the proposed method. This work lays the foundation for developing safer and smarter continuum robots, enabling broader clinical adoption in complex environments.
Paper Structure (31 sections, 22 equations, 4 figures, 2 tables, 1 algorithm)

This paper contains 31 sections, 22 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. State-of-the-Art Review
  3. Challenges and Contributions
  4. Methods
  5. Basic Principles
  6. Bionic principles
  7. Basic Mathematical Principles
  8. Proximal Forces Decoupling
  9. Mechanical Balance
  10. Beam Deformation in Local Coordinate System
  11. Geometric Constraints and Optimization
  12. Results
  13. Device
  14. Shape Estimation
  15. Shape Estimation Under Force Constraints
  16. ...and 16 more sections

Figures (4)

  • Figure 1: Basic principles of bionics and system framework. (a) Biological mapping of robotic sensing components. (b) Principle of implementing proprioception. (c) Mechanical balance between cable and beam. (d) Capstan friction model to describe cable tension transmission.
  • Figure 2: Test benches. (a) Experimental platform. (b) Shape prediction of the robot under force control. (c) Elastic elongation of the driving cable and elastic deformation of the component. (d) Shape prediction of the robot under displacement control.
  • Figure 3: Force prediction test results. (a) Robot shape under tip load. (b) Estimation of tip load. (c) Contact force and contact point prediction for active contact. (d) Contact force and contact point prediction for passive contact. (e) Reducing perception errors through repeated weighing movements. (f) Improvement of contact location estimation through friction redistribution.
  • Figure 4: Three-dimensional force estimation and stability verification. (a)–(c) Three-dimensional force estimation of continuum robots with different diameters. (d)-(e) Perception framework validation under different contact conditions. (f) The performance of the perception framework in multi-point contact. (g) Performance of the perception framework at different speeds.