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.

翻译：本研究提出了一种新型共享控制系统，用于实现微创手术中的匙孔式对接操作。该系统结合商用相机、抗遮挡姿态估计技术及手眼信息融合方法，旨在提升对接精度与力顺应性安全。通过自监督数据集训练手眼信息融合网络模型，我们的姿态估计方法在精度上优于传统方案（包括纯视觉观测法、手眼标定及常规状态估计滤波器）。在真实仿体实验中，该方法展现出显著优势：相比对照组，位置分散度（1.23±0.81 mm vs. 2.47±1.22 mm）与力分散度（0.78±0.57 N vs. 1.15±0.97 N）均显著降低。这些半自主协同操控技术的进展有效增强了交互稳定性。本研究提出的抗干扰、高稳定、高精度解决方案不仅适用于腹腔镜手术，还可推广至其他微创介入治疗领域。