用新方法提升远程手术机器人位置估计精度，抗网络延迟干扰。
Enhanced Position Estimation in Tactile Internet-Enabled Remote Robotic Surgery Using MOESP-Based Kalman Filter
- 结合MOESP与卡尔曼滤波，无需先验模型直接建模手术机器人动态
- 在模拟网络延迟、抖动和丢包下仍保持95%以上估计准确率
- 适合研究触觉互联网中高实时性控制的工程师与医疗机器人开发者
在触觉互联网环境下，实时精准估计患者侧机械臂位置是远程手术中的关键挑战。本文提出一种基于多变量输出误差状态空间（MOESP）方法进行系统辨识，并融合卡尔曼滤波的位置估计算法。该方法不依赖系统动力学先验知识，而是利用JIGSAW数据集及主操作手（MTM）输入，直接构建患者侧机械臂（PSM）的状态空间模型。在模拟网络延迟、抖动和丢包等真实触觉互联网条件下的测试表明，该方法显著提升了卡尔曼滤波在不确定性环境下的鲁棒性与准确性，状态估计精度超过95%。
原文摘要 · Abstract (English)
Accurately estimating the position of a patient's side robotic arm in real time during remote surgery is a significant challenge, especially within Tactile Internet (TI) environments. This paper presents a new and efficient method for position estimation using a Kalman Filter (KF) combined with the Multivariable Output-Error State Space (MOESP) method for system identification. Unlike traditional approaches that require prior knowledge of the system's dynamics, this study uses the JIGSAW dataset, a comprehensive collection of robotic surgical data, along with input from the Master Tool Manipulator (MTM) to derive the state-space model directly. The MOESP method allows accurate modeling of the Patient Side Manipulator (PSM) dynamics without prior system models, improving the KF's performance under simulated network conditions, including delays, jitter, and packet loss. These conditions mimic real-world challenges in Tactile Internet applications. The findings demonstrate the KF's improved resilience and accuracy in state estimation, achieving over 95 percent accuracy despite network-induced uncertainties.
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