Vibration-Assisted Hysteresis Mitigation for Achieving High Compensation Efficiency

2025-03-04

AI Summary

Tendon-sheath mechanisms (TSMs) in minimally invasive surgery suffer from nonlinear hysteresis and trajectory tracking errors due to friction, backlash, and tendon elongation. Method: This paper proposes a novel active compensation paradigm leveraging controllable axial vibration—introducing mechanical vibration into the TSM compensation framework for the first time—to mitigate static friction and dead-zone effects. The approach integrates vibration actuation control with a lightweight Temporal Convolutional Network (TCN) for data-driven modeling, substantially reducing reliance on precise physical parameter identification. Results: Experiments demonstrate that vibration alone reduces RMSE by 23.41%; when combined with TCN, MAE decreases by 85.2%, model parameters are reduced by over 60%, and both tracking accuracy and robustness improve significantly. The method exhibits superior generalizability and system scalability compared to conventional approaches.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Active Learning
• Computer Vision: Motion & Tracking

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Mitigates hysteresis in tendon-sheath mechanisms for surgical robots.
• Reduces tracking errors caused by friction and tendon elongation.
• Improves trajectory accuracy with vibration-assisted compensation.

Innovation

• Vibration-assisted hysteresis compensation reduces friction.
• Temporal Convolutional Network enhances compensation accuracy.
• Combined approach significantly decreases trajectory tracking errors.