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

TL;DR

This paper presents a semi-autonomous laparoscopic robot docking system that fuses learned hand-eye information to improve precision and safety, validated through real-world phantom experiments showing significant reductions in positional and force dispersion.

Contribution

The study introduces a novel hand-eye information fusion network trained with a self-supervised dataset, enhancing pose estimation accuracy and docking stability in minimally invasive surgery.

Findings

Improved pose estimation accuracy over traditional methods.

Reduced position dispersion from 2.47mm to 1.23mm.

Decreased force dispersion from 1.15N to 0.78N.

Abstract

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.