Physics-Informed LSTM-Based Delay Compensation Framework for Teleoperated UGVs

📄 arXiv: 2402.16587v1 📥 PDF

作者: Ahmad Abubakar, Yahya Zweiri, AbdelGafoor Haddad, Mubarak Yakubu, Ruqayya Alhammadi, Lakmal Seneviratne

分类: eess.SY

发布日期: 2024-02-26

💡 一句话要点

提出基于物理信息的LSTM延迟补偿框架以解决UGV远程操作中的延迟问题

🎯 匹配领域: 支柱一：机器人控制 (Robot Control)

关键词: 物理信息 长短期记忆网络 延迟补偿 无人地面车辆 远程操作 动态控制 非线性建模

📋 核心要点

  1. 现有的无模型预测框架在捕捉非线性和时间动态行为方面存在局限，导致UGV控制性能下降。
  2. 本文提出的PiLSTM框架结合了物理约束与LSTM结构，有效补偿网络延迟，提高了控制精度。
  3. 实验验证显示，该框架在闭环场景中有效恢复了控制的高保真度，显著提升了指令跟踪性能。

📝 摘要（中文）

低速无人地面车辆（UGVs）在软土上进行双向远程操作对于月球探索等应用至关重要，但网络传输延迟会影响闭环控制的高保真度，导致指令跟踪性能下降。为了解决这一挑战，本文提出了一种新颖的预测框架，采用物理信息长短期记忆（PiLSTM）网络，设计出有效补偿大延迟的双向远程操作控制。与传统的无模型预测框架相比，该方法结合了LSTM结构与物理约束，能够更好地捕捉非线性和时间动态行为。实验结果表明，PiLSTM框架在开放环节的延迟补偿上比传统方法提高了26.1%。

🔬 方法详解

问题定义：本文旨在解决低速UGV在软土上双向远程操作中，由于网络延迟导致的控制精度下降问题。现有的无模型预测方法在面对非线性和时间动态行为时表现不佳，无法有效补偿延迟。

核心思路：提出的PiLSTM框架通过结合物理信息与LSTM网络，能够更好地捕捉复杂的动态行为，从而实现对大延迟的有效补偿。该设计旨在提高控制系统的响应速度和精度。

技术框架：框架包含四个预测器，其中两个用于补偿前向延迟，另外两个用于补偿后向延迟。整体流程包括数据收集、模型训练和实时控制。

关键创新：最重要的创新在于将物理约束引入LSTM结构，使得模型在处理非线性动态行为时具备更好的泛化能力，显著提升了延迟补偿效果。

关键设计：在网络结构上，PiLSTM框架采用了多层LSTM单元，并设计了特定的损失函数以优化延迟补偿性能。关键参数设置经过多次实验调整，以确保模型的稳定性和准确性。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，PiLSTM框架在开放环节的延迟补偿上比传统的无模型预测方法提高了26.1%。在闭环场景中，该框架有效恢复了控制的高保真度，显著提升了指令跟踪性能，验证了其在实际应用中的有效性。

🎯 应用场景

该研究的潜在应用领域包括月球探索、灾后救援和危险环境下的远程操作等。通过提高UGV在复杂环境中的控制精度，该框架能够显著提升远程操作的安全性和效率，具有重要的实际价值和广泛的应用前景。

📄 摘要（原文）

Bilateral teleoperation of low-speed Unmanned Ground Vehicles (UGVs) on soft terrains is crucial for applications like lunar exploration, offering effective control of terrain-induced longitudinal slippage. However, latency arising from transmission delays over a network presents a challenge in maintaining high-fidelity closed-loop integration, potentially hindering UGV controls and leading to poor command-tracking performance. To address this challenge, this paper proposes a novel predictor framework that employs a Physics-informed Long Short-Term Memory (PiLSTM) network for designing bilateral teleoperator controls that effectively compensate for large delays. Contrasting with conventional model-free predictor frameworks, which are limited by their linear nature in capturing nonlinear and temporal dynamic behaviors, our approach integrates the LSTM structure with physical constraints for enhanced performance and better generalization across varied scenarios. Specifically, four distinct predictors were employed in the framework: two compensate for forward delays, while the other two compensate for backward delays. Due to their effectiveness in learning from temporal data, the proposed PiLSTM framework demonstrates a 26.1\ improvement in delay compensation over the conventional model-free predictors for large delays in open-loop case studies. Subsequently, experiments were conducted to validate the efficacy of the framework in close-loop scenarios, particularly to compensate for the real-network delays experienced by teleoperated UGVs coupled with longitudinal slippage. The results confirm the proposed framework is effective in restoring the fidelity of the closed-loop integration. This improvement is showcased through improved performance and transparency, which leads to excellent command-tracking performance.