用Informer模型提升远程手术机器人定位精度，应对网络延迟与丢包。
A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model
- 基于Informer框架，融合注意力蒸馏与概率稀疏机制，高效预测机械臂位置。
- 在JIGSAWS数据集上，多种网络条件下预测准确率超90%。
- 适合需实时高精度控制的远程手术系统，尤其适用于触觉互联网场景。
在触觉互联网（Tactile Internet, TI）环境下，远程机器人手术中对患者侧机械臂位置的精确实时估计至关重要。本文提出一种基于Transformer架构Informer的预测模型，用于实现高精度、高效率的位置估计。同时，引入四状态隐马尔可夫模型（4-State HMM）模拟真实网络环境中的包丢失情况。该方法通过可微优化层将能量效率、运动平滑性与鲁棒性等约束嵌入训练过程，解决网络延迟、抖动和丢包带来的挑战。Informer采用概率稀疏注意力（ProbSparse attention）、注意力蒸馏（attention distilling）和生成式解码器，在保持较低计算复杂度O(L log L)的同时聚焦关键位置特征。在JIGSAWS数据集上的评估表明，该方法在多种网络条件下预测准确率超过90%。相比TCN、RNN和LSTM等模型，Informer在处理位置预测任务及满足实时性要求方面表现更优，适用于触觉互联网支持的远程手术应用。
原文摘要 · Abstract (English)
Precise and real-time estimation of the robotic arm's position on the patient's side is essential for the success of remote robotic surgery in Tactile Internet (TI) environments. This paper presents a prediction model based on the Transformer-based Informer framework for accurate and efficient position estimation. Additionally, it combines a Four-State Hidden Markov Model (4-State HMM) to simulate realistic packet loss scenarios. The proposed approach addresses challenges such as network delays, jitter, and packet loss to ensure reliable and precise operation in remote surgical applications. The method integrates the optimization problem into the Informer model by embedding constraints such as energy efficiency, smoothness, and robustness into its training process using a differentiable optimization layer. The Informer framework uses features such as ProbSparse attention, attention distilling, and a generative-style decoder to focus on position-critical features while maintaining a low computational complexity of O(L log L). The method is evaluated using the JIGSAWS dataset, achieving a prediction accuracy of over 90 percent under various network scenarios. A comparison with models such as TCN, RNN, and LSTM demonstrates the Informer framework's superior performance in handling position prediction and meeting real-time requirements, making it suitable for Tactile Internet-enabled robotic surgery.
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