基于视觉的微创遥手术力估计：通过接触检测与局部刚度模型
Vision-Based Force Estimation for Minimally Invasive Telesurgery Through Contact Detection and Local Stiffness Models
AI总结：
本文提出一种基于视觉的接触条件力估计方法，通过深度学习结合末端执行器位置与关节扭矩信息，在遥手术中实现超90%接触检测准确率和低于10%力预测误差，助力触觉反馈与技能评估。
摘要：
在微创遥手术中，由于体内末端执行器力感知的复杂性，获取准确的力信息十分困难。这分别限制了触觉反馈和基于力的自动化性能指标的开发与实现。使用深度学习的基于视觉的力感知方法是末端执行器内在力感知的一种有前途的替代方案。然而，它们泛化到新场景的能力有限，并且需要学习难以获取的高质量力传感器训练数据。为了应对这些挑战，本文提出了一种新颖的基于视觉的接触条件方法，用于遥手术环境中的力估计。我们的方法利用带有人类标签的监督学习和末端执行器位置数据来训练深度神经网络。这些训练模型的预测结果可选择性地与机器人关节扭矩信息相结合，从而间接从视觉数据中估计力。我们将该方法与真实力传感器数据进行基准比较，并通过以数据高效的方式微调到新的手术场景来证明其泛化性。我们的方法在接触检测上表现出超过90%的准确率，力预测误差低于10%。这些结果表明，接触条件力估计在临床环境中的感觉替代触觉反馈和组织处理技能评估方面具有潜在的有用性。
英文摘要：
In minimally invasive telesurgery, obtaining accurate force information is difficult due to the complexities of in-vivo end effector force sensing. This constrains development and implementation of haptic feedback and force-based automated performance metrics, respectively. Vision-based force sensing approaches using deep learning are a promising alternative to intrinsic end effector force sensing. However, they have limited ability to generalize to novel scenarios, and require learning on high-quality force sensor training data that can be difficult to obtain. To address these challenges, this paper presents a novel vision-based contact-conditional approach for force estimation in telesurgical environments. Our method leverages supervised learning with human labels and end effector position data to train deep neural networks. Predictions from these trained models are optionally combined with robot joint torque information to estimate forces indirectly from visual data. We benchmark our method against ground truth force sensor data and demonstrate generality by fine-tuning to novel surgical scenarios in a data-efficient manner. Our methods demonstrated greater than 90% accuracy on contact detection and less than 10% force prediction error. These results suggest potential usefulness of contact-conditional force estimation for sensory substitution haptic feedback and tissue handling skill evaluation in clinical settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
