基于物理信息LSTM的遥操作无人地面车辆延迟补偿框架
Physics-Informed LSTM-Based Delay Compensation Framework for Teleoperated UGVs
AI总结：
本文提出基于物理信息LSTM的预测器框架，通过四个预测器分别补偿前后向时延，显著提升软地形遥操作UGV在大时延下的闭环跟踪性能。
摘要：
在软地形上对低速无人地面车辆（UGVs）进行双边遥操作对于月球探测等应用至关重要，能够有效控制由地形引起的纵向滑移。然而，网络传输延迟所产生的时延对保持高保真闭环集成构成了挑战，可能妨碍UGV控制并导致较差的指令跟踪性能。为应对这一挑战，本文提出了一种新颖的预测器框架，该框架采用物理信息长短期记忆（PiLSTM）网络来设计双边遥操作控制器，从而有效补偿大时延。与传统的无模型预测器框架相比，后者在捕捉非线性和时间动态行为方面受限于其线性特性，我们的方法将LSTM结构与物理约束相结合，以提升性能并在不同场景下实现更好的泛化。具体而言，框架中采用了四个不同的预测器：两个用于补偿前向时延，另外两个用于补偿后向时延。由于其在从时间数据中学习的有效性，所提出的PiLSTM框架在开环案例研究中针对大时延的延迟补偿相较于传统无模型预测器提升了26.1%。随后，进行了实验以验证该框架在闭环场景中的有效性，特别是补偿遥操作UGV在耦合纵向滑移情况下所经历的真实网络延迟。结果证实，所提出的框架能够有效恢复闭环集成的保真度。这一改进通过提升的性能和透明度得以展示，从而实现了出色的指令跟踪性能。
英文摘要：
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.
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。
