场景图驱动的触觉反馈：基于物理模拟iOCT的机器人眼科手术安全性增强
Scene Graph-Driven Haptic Feedback for Safety Enhancement in Robotic Ophthalmic Surgery via Physically Simulated iOCT
浏览论文内容
中文总结 AI 辅助
本文提出基于场景图和物理模拟iOCT的触觉反馈系统，通过规则引擎生成状态相关反馈，减少机器人眼科手术中针头对准误差14%并提升可用性评分8%，增强手术安全性。
中文摘要 AI 辅助
机器人眼科手术具有高精度，但通过将外科医生与器械解耦，引入了“感觉鸿沟”，导致触觉反馈的丧失。本文提出了一种新颖的触觉反馈系统，用于视网膜下注射任务，利用场景图（SG）。该系统通过分析物理模拟的术中光学相干断层扫描（iOCT）图像流，构建实时手术场景图，从而弥合感觉鸿沟。场景图作为手术场景的语义抽象层，随后被一个确定性的、基于规则的引擎利用，在机器人输入设备上生成状态相关的触觉反馈。该系统在一项用户研究（N=16）中进行了评估，使用了拟人头部模型和定制构建的手术机器人。结果表明，场景图驱动的触觉反馈提高了手术精度，将针头对准误差降低了14%（p = 0.044），并将系统可用性量表（SUS）评分提高了8%（p = 0.015），同时保持了相当的任务完成时间。针头轨迹分析揭示了更安全的“先对准后接近”策略的出现，其中我们的触觉负强化促使用户在接近视网膜目标之前微调工具的轨迹。这项工作表明，场景图可以有效地作为机器人显微手术中实时、增强安全性的情境感知触觉反馈的直接计算基础。
英文摘要
Robotic ophthalmic surgery offers high precision but introduces a "sensory gap" by decoupling the surgeon from their instrument, resulting in a loss of tactile feedback. This paper presents a novel haptic feedback system for subretinal injection tasks leveraging Scene Graphs (SG). The system bridges the sensory gap by analyzing a physically simulated intraoperative Optical Coherence Tomography (iOCT) feed to construct a real-time surgical SG. The SG serves as a semantic abstraction layer for the surgical scene, which is then utilized by a deterministic, rule-based engine to generate state-dependent haptic feedback on a robotic input device. The system was evaluated in a user study (N=16) using an anthropomorphic head phantom and a custom-built surgical robot. Results demonstrate that the SG-driven haptic feedback improved surgical precision, reducing needle alignment error by 14% (p = 0.044) and improving System Usability Scale (SUS) scores by 8% (p = 0.015), while maintaining comparable task completion times. A needle trajectory analysis revealed the emergence of a safer "Align-then-Approach" strategy, in which our haptic negative reinforcement prompted users to fine-tune the tool's trajectory before approaching the retinal target. This work suggests that SGs can effectively serve as the direct computational foundation for real-time, safety-enhancing context-aware haptic feedback in robotic microsurgery.
发表机构
- Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM)（慕尼黑工业大学慕尼黑机器人与机器智能研究所）
- TUM School of Engineering and Design, Technical University of Munich (TUM)（慕尼黑工业大学工程与设计学院）
机构由 AI 辅助整理，请以论文原文为准。
