Semi-Autonomous Laparoscopic Robot Docking with Learned Hand-Eye Information Fusion
<|reference_start|>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 precision solution with potential applications extending beyond laparoscopic surgery to other minimally invasive procedures.<|reference_end|>
@article{tian2024semi-autonomous,
    title={Semi-Autonomous Laparoscopic Robot Docking with Learned Hand-Eye
  Information Fusion},
    author={Huanyu Tian, Martin Huber, Christopher E. Mower, Zhe Han, Changsheng
  Li, Xingguang Duan, Christos Bergeles},
    journal={arXiv preprint arXiv:2405.05817},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05817},
    primaryClass={cs.RO}
}