Vibration-Assisted Hysteresis Mitigation for Achieving High Compensation Efficiency

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.

SAM: Semi-Active Mechanism for Extensible Continuum Manipulator and Real-time Hysteresis Compensation Control Algorithm

Cable-Driven Continuum Manipulators (CDCMs) enable scar-free procedures via natural orifices and improve target lesion accessibility through curved paths. However, CDCMs face limitations in workspace and control accuracy due to non-linear cable effects causing hysteresis. This paper introduces an extensible CDCM with a Semi-active Mechanism (SAM) to expand the workspace via translational motion without additional mechanical elements or actuation. We collect a hysteresis dataset using 8 fiducial markers and RGBD sensing. Based on this dataset, we develop a real-time hysteresis compensation control algorithm using the trained Temporal Convolutional Network (TCN) with a 1ms time latency, effectively estimating the manipulator's hysteresis behavior. Performance validation through random trajectory tracking tests and box pointing tasks shows the proposed controller significantly reduces hysteresis by up to 69.5% in joint space and approximately 26% in the box pointing task.