Vibration-Assisted Hysteresis Mitigation for Achieving High Compensation Efficiency

TL;DR

This paper tackles hysteresis in tendon-sheath mechanisms (TSMs) used in MIS, caused by friction, backlash, and tendon elongation, which degrades trajectory accuracy. It introduces a vibration-assisted approach that applies controlled longitudinal tendon vibrations to mitigate nonlinearities, and couples this with a data-driven compensation pipeline based on Temporal Convolutional Networks (TCNs). Experimental results show that vibration reduces hysteresis on random trajectories by up to 23.41% in RMSE, and that using a vibration-trained TCN reduces MAE by 85.2% under identical settings, while even small models with vibration outperform large models without. The findings demonstrate a scalable, practical strategy to improve TSM-based robotic performance, enabling more efficient modeling and broader MIS applications, with potential extensions to other tendon-driven robotic systems.

Abstract

Tendon-sheath mechanisms (TSMs) are widely used in minimally invasive surgical (MIS) applications, but their inherent hysteresis-caused by friction, backlash, and tendon elongation-leads to significant tracking errors. Conventional modeling and compensation methods struggle with these nonlinearities and require extensive parameter tuning. To address this, we propose a vibration-assisted hysteresis compensation approach, where controlled vibrational motion is applied along the tendon's movement direction to mitigate friction and reduce dead zones. Experimental results demonstrate that the exerted vibration consistently reduces hysteresis across all tested frequencies, decreasing RMSE by up to 23.41% (from 2.2345 mm to 1.7113 mm) and improving correlation, leading to more accurate trajectory tracking. When combined with a Temporal Convolutional Network (TCN)-based compensation model, vibration further enhances performance, achieving an 85.2% reduction in MAE (from 1.334 mm to 0.1969 mm). Without vibration, the TCN-based approach still reduces MAE by 72.3% (from 1.334 mm to 0.370 mm) under the same parameter settings. These findings confirm that vibration effectively mitigates hysteresis, improving trajectory accuracy and enabling more efficient compensation models with fewer trainable parameters. This approach provides a scalable and practical solution for TSM-based robotic applications, particularly in MIS.

Figures (7)

• Figure 1: Illustration of the tendon-sheath mechanism (TSM) with applied vibration. The enlarged view highlights the controlled vibrational motion along the tendons, aimed at reducing friction and hysteresis.
• Figure 2: Hardware configuration of the Input and Output Modules. (a) Overall Hardware configuration (b) The Input Module includes an eccentric axis, crank slider, vibration motor for tendon vibration. (c) The Output Module consists of tension spring for tendon load. (d) Crank-slider structure of the vibration mechanism designed to induce vibration in tension.
• Figure 3: Control architecture of the TSM system, consisting of a motion generator (55Hz) for trajectory processing, a control loop (1kHz) for motion and vibration control, and an observation loop (55Hz) for real-time system monitoring.
• Figure 4: Graph of Hysteresis effect on Sinusoid Trajectory, (1-a) Distal position in the time domain from the initial tension experiment. (1-b) Tension hysteresis observed in the same experiment. (1-c) Hysteresis observed in relation to the position. (2-a) Time-domain graph illustrating the experiment related to frequency and vibration. (2-b) The relationship between input Tension and output Tension. (2-c) Hysteresis loop showing the correlation between input and output positions.
• Figure 5: Trajectory tracking performance under different vibration frequencies. The application of vibration reduces hysteresis and improves tracking accuracy. The black box highlights the reduction in dead zones.