Abstract
Ophthalmic surgical robots offer superior stability and precision by reducing the natural hand tremors of human surgeons, enabling delicate operations in confined surgical spaces. Despite the advancements in developing vision- and force-based control methods for surgical robots, preoperative navigation remains heavily reliant on manual operation, limiting the consistency and increasing the uncertainty. Existing eye gaze estimation techniques in the surgery, whether traditional or deep learning-based, face challenges including dependence on additional sensors, occlusion issues in surgical environments, and the requirement for facial detection. To address these limitations, this study proposes an innovative eye localization and tracking method that combines machine learning with traditional algorithms, eliminating the requirements of landmarks and maintaining stable iris detection and gaze estimation under varying lighting and shadow conditions. Extensive real-world experiment results show that our proposed method has an average estimation error of 0.58 degrees for eye orientation estimation and 2.08-degree average control error for the robotic arm's movement based on the calculated orientation.
Abstract (translated)
眼科手术机器人通过减少人类外科医生的自然手颤，提供卓越的稳定性和精度，使精细的操作能够在狭小的手术空间中进行。尽管在开发基于视觉和力反馈的控制方法方面取得了进展，但术前导航仍然严重依赖手动操作，这限制了一致性并增加了不确定性。现有的眼科手术中的目光估计技术，无论是传统的还是基于深度学习的方法，都面临着诸如对额外传感器的依赖、手术环境中遮挡问题以及面部检测需求等挑战。 为了克服这些局限性，本研究提出了一种创新的眼部定位和跟踪方法，该方法结合了机器学习与传统算法，消除了地标的需求，并在不同的光照和阴影条件下保持稳定的虹膜检测和目光估计。大量的真实世界实验结果表明，我们提出的方法在眼部方向估计上的平均误差为0.58度，在根据计算方向控制机械臂运动时的平均控制误差为2.08度。
URL
https://arxiv.org/abs/2507.00635
