  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
precision solution with potential applications extending beyond laparoscopic
surgery to other minimally invasive procedures.
