Home /Research /Design, Modeling, and Experimental Validation of a Bio-Inspired Rigid–Flexible Continuum Robot Driven by Flexible Shaft Tension–Torsion Synergy
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Design, Modeling, and Experimental Validation of a Bio-Inspired Rigid–Flexible Continuum Robot Driven by Flexible Shaft Tension–Torsion Synergy

Jiaxiang Dong, Quanquan Liu, Peng Li, Chunbao Wang, Xuezhi Zhao, Xiping Hu

Year
2025
Citations
3
Access
Open access

Abstract

This paper presents a bio-inspired rigid-flexible continuum robot driven by flexible shaft tension-torsion synergy, tackling the trade-off between actuation complexity and flexibility in continuum robots. Inspired by the muscular arrangement of octopus arms, enabling versatile multi-degree-of-freedom (DoF) movements, the robot achieves 6-DoF motion and 1-DoF gripper opening and closing movement with only six flexible shafts, simplifying actuation while boosting dexterity. A comprehensive kinetostatic model, grounded in Cosserat rod theory, is developed; this model explicitly incorporates the coupling between the spinal rods and flexible shafts, the distributed gravitational effects of spacer disks, and friction within the guide tubes. Experimental validation using a physical prototype reveals that accounting for spacer disk gravity diminishes the maximum shape prediction error from 20.56% to 0.60% relative to the robot's total length. Furthermore, shape perception experiments under no-load and 200 g load conditions show average errors of less than 2.01% and 2.61%, respectively. Performance assessments of the distal rigid joint showcased significant dexterity, including a 53° grasping range, 360° continuous rotation, and a pitching range from -40° to +45°. Successful obstacle avoidance and long-distance target reaching experiments further demonstrate the robot's effectiveness, highlighting its potential for applications in medical and industrial fields.

Keywords

RobotTorsion (gastropod)TorqueTorsion springComputer scienceSimulationControl theory (sociology)Mechanical engineeringEngineeringArtificial intelligence

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