TY  - EJOU
AU  - Lyu, Junmin 
AU  - Bao, Feng 
AU  - Xu, Guangyu 
AU  - Lu, Siyu 
AU  - Yang, Bo 
AU  - Liu, Yuxin 
AU  - Zheng, Wenfeng 

TI  - A Learning-Driven Visual Servoing Framework for Latency Compensation in Image-Guided Teleoperation
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 146
IS  - 2
SN  - 1526-1506

AB  - Robust teleoperation in image-guided interventions faces critical challenges from latency, deformation, and the quasi-periodic nature of physiological motion. This paper presents a fully integrated, latency-aware visual servoing system leveraging stereo vision, hand–eye calibration, and learning-based prediction for motion-compensated teleoperation. The system combines a calibrated binocular camera setup, dual robotic arms, and a predictive control loop incorporating Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) models. Through experiments using both <i>in vivo</i> and phantom datasets, we quantitatively assess the prediction accuracy and motion-compensation performance of both models. Results show that TCNs deliver more stable and precise tracking, especially on regular trajectories, while LSTMs exhibit robustness under quasi-periodic dynamics. By matching prediction horizons to system latency, the approach significantly reduces peak and steady-state tracking errors, demonstrating practical feasibility for deploying prediction-augmented servoing in teleoperated surgical.
KW  - Teleoperation system; motion prediction; surgical robot; visual servoing; learning-based control

DO  - 10.32604/cmes.2025.075178