Motion Compensation and Adaptive Force Control Via iOCT–FBG Sensor Fusion for Robotic Subretinal Injection

Abstract

Subretinal injection is a highly delicate procedure that demands micron-level precision to avoid irreversible retinal damage. Current robotic systems achieve accurate positioning but remain limited by retinal motion and the lack of tip-force feedback. We present the first adaptive tip-force compensation framework for robotic subretinal injection, fusing intraoperative optical coherence tomography (iOCT) vision with fiber Bragg grating (FBG) force sensing. Our architecture integrates a finite-state machine (FSM) for surgical phase coordination, a Long Short-Term Memory (LSTM) enhanced residual Kalman filter for real-time motion prediction, and an adaptive compliance estimator for safe force regulation. Compared to previous vision-only and force-only method, ex vivo experiments on porcine eyes demonstrate robust improvements: the root-mean-square tracking error reduced by 40% (to 18.5μm), the maximum absolute error lowered by 2.5 times, and 96.7% of tip forces maintained within ± 0.7mN. Control delays were minimized to 0.25s, enabling low-latency corrections beyond freehand capabilities. Our system enhances precision and safety in fragile retinal tissues, advancing the potential for reliable robot-assisted surgeries for retinal diseases.