用数字孪生降低远程手术延迟，提升操作精度与医生体验。
RoboTwin: A Robotic Teleoperation Framework Using Digital Twins
- 医生操控本地患者侧数字孪生，实现低延迟操作。
- 双数字孪生框架使手术准确率提升，认知负荷显著下降。
- 对象识别数据量减少25倍，适合远程医疗与高安全场景。
机器人手术对主刀医生带来显著认知负担，尤其在远程操作时，因两端相隔数百至数千公里，延迟问题加剧，影响手术质量。本研究提出双数字孪生（DT）框架，构建仿真环境与远程操控系统。医生通过本地部署的患者侧数字孪生进行视觉控制，实现最低延迟操作；第二数字孪生兼具双重功能：一是防范操作失误带来的风险，二是将已知与未知物体坐标反馈至医生侧数字孪生。实验结果表明，采用该框架后，操作准确性和用户体验明显提升。基于NASA-TLX量表评估显示，手术质量显著改善，可能归因于认知负担降低。此外，操作端的对象识别数据传输速率较传统方式降低25倍。
原文摘要 · Abstract (English)
Robotic surgery imposes a significant cognitive burden on the surgeon. This cognitive burden increases in the case of remote robotic surgeries due to latency between entities and thus might affect the quality of surgery. Here, the patient side and the surgeon side are geographically separated by hundreds to thousands of kilometres. Real-time teleoperation of robots requires strict latency bounds for control and feedback. We propose a dual digital twin (DT) framework and explain the simulation environment and teleoperation framework. Here, the doctor visually controls the locally available DT of the patient side and thus experiences minimum latency. The second digital twin serves two purposes. Firstly, it provides a layer of safety for operator-related mishaps, and secondly, it conveys the coordinates of known and unknown objects back to the operator's side digital twin. We show that teleoperation accuracy and user experience are enhanced with our approach. Experimental results using the NASA-TLX metric show that the quality of surgery is vastly improved with DT, perhaps due to reduced cognitive burden. The network data rate for identifying objects at the operator side is 25x lower than normal.
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