Semi-Autonomous Laparoscopic Robot Docking With Learned Hand-Eye Information Fusion

In this study, we introduce a novel shared-control system for key-hole docking operations, combining a commercial camera with occlusion-robust pose estimation and a hand-eye information fusion technique. This system is used to enhance docking precision and force-compliance safety. To train a hand-eye information fusion network model, we generated a self-supervised dataset using this docking system. After training, our pose estimation method showed improved accuracy compared to traditional methods, including observation-only approaches, hand-eye calibration, and conventional state estimation filters. In real-world phantom experiments, our approach demonstrated its effectiveness with reduced position dispersion (1.23 $\pm$ 0.81 mm vs. 2.47 $\pm$ 1.22 mm) and force dispersion (0.78 $\pm$ 0.57 N vs. 1.15 $\pm$ 0.97 N) compared to the control group. These advancements in semi-autonomy co-manipulation scenarios enhance interaction and stability. The study presents an anti-interference, steady, and precise solution with potential applications extending beyond laparoscopic surgery to other minimally invasive procedures.