arXiv subjects

Myeongbo Park

Publications and source records attributed to Myeongbo Park.

4 recordsLinked to original sources

arXiv2026

SSTL: Self-Sensing Tendon Loop for Hysteresis Modeling and Compensation in Tendon-Sheath Mechanisms

Flexible endoscopic robots enable minimally invasive access through natural orifices, but their control accuracy is limited by configuration-dependent hysteresis in the tendon-sheath mechanisms (TSMs). Tendon-sheath friction and tendon elasticity induce a systematic discrepancy between the proximal actuation input and distal output, and this discrepancy varies with the insertion tube configuration. To address this challenge, this paper proposes the Self-Sensing Tendon Loop (SSTL), a double-pass tendon loop routed through the insertion tube and wrapped around a distal pulley, and returned to the proximal end. The loop structure allows both the input and output tensions of the SSTL to be measured proximally, thereby providing an input-output tension profile without requiring distal force or fiber-optic sensors. Because the SSTL shares the same routing path as the actuation TSM, the two TSMs exhibit strongly correlated hysteresis behaviors. From the SSTL tension profile, a learning-based mapping estimates the configuration-dependent hysteresis parameters of the actuation TSM, which are then used by a feedforward controller to compensate for actuation hysteresis. We validate the proposed method by tracking actuation tendon tension under three different insertion tube configurations. Across sinusoidal and random trajectories, the proposed method reduces average RMSE by 88.1% compared with the uncompensated baseline, achieving 97.8% of the performance of direct identification, which requires direct measurement of the input and output tension profile of the actuation TSM.

cs.RO↗

arXiv2026

MarUco: A Markerless 6D Pose Estimation Framework for Closed-Loop Control of Surgical Continuum Manipulators

Flexible endoscopic continuum manipulators offer high dexterity and access to complex anatomy, but nonlinear hysteresis limits feedforward control accuracy. Closed-loop control can compensate for these errors but requires accurate six-degree-of-freedom (6D) end-effector pose feedback. We present MarUco, a markerless 6D pose estimation framework for closed-loop control using only stereo vision during operation. A photorealistic pseudo-rigid-body simulation pipeline generates large-scale annotated training data without manual labeling. A multifeature fusion network integrates masks, keypoints, heatmaps, and bounding boxes from both stereo views to estimate an initial pose, followed by a learned single-pass render-and-compare module that refines the pose without iterative optimization. Kinematics-free hand-eye calibration estimates camera-to-robot-base extrinsics, and self-supervised adaptation uses unlabeled real stereo pairs to mitigate sim-to-real pose error. Across 1,000 real samples, MarUco achieves translation and rotation errors of 0.78 $\pm$ 0.50 mm and 3.07 $\pm$ 1.25{\deg}, respectively, with a total processing time of 52.4 ms per stereo pair. In closed-loop experiments over eight reference paths, MarUco achieves a mean terminal translation error of 1.8 mm, an 88% reduction relative to uncompensated open-loop control. To the best of our knowledge, this is the first markerless position-based visual servoing framework for continuum manipulators.

cs.RO↗

arXiv2025

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.

cs.RO↗

arXiv2024

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

Cable-Driven Continuum Manipulators (CDCMs) enable scar-free procedures but face limitations in workspace and control accuracy due to hysteresis. We introduce an extensible CDCM with a Semi-active Mechanism (SAM) and develop a real-time hysteresis compensation control algorithm using a Temporal Convolutional Network (TCN) based on data collected from fiducial markers and RGBD sensing. Performance validation shows the proposed controller significantly reduces hysteresis by up to 69.5% in random trajectory tracking test and approximately 26% in the box pointing task. The SAM mechanism enables access to various lesions without damaging surrounding tissues. The proposed controller with TCN-based compensation effectively predicts hysteresis behavior and minimizes position and joint angle errors in real-time, which has the potential to enhance surgical task performance.

cs.RO↗