Mobility of teleoperated unmanned ground vehicles can be significantly compromised under large communication delays, if the delays are not compensated. This article considers a recently developed delay compensation theory and presents its first empirical evaluation in improving mobility and drivability of a high-speed teleoperated vehicle under large delays. The said delay compensation theory is a predictor-based framework. Two realizations of this framework are considered: a model-free realization that relies only on model-free predictors, and a blended realization, where the heading predictions from the model-free predictor are blended with those from a steering-model-based feedforward predictor for a more accurate prediction of the vehicle heading. A teleoperated track-following task is designed in a human-in-the-loop simulation platform. This platform is used to compare the teleoperation performance with and without the predictor-based framework under both constant and varying delays. Through repeated measurement analysis of variance, it is concluded that the predictor-based framework is effective in achieving a higher vehicle speed, more accurate lateral control, and better drivability as indicated by the three performance metrics of track completion time, track keeping error, and steering control effort, respectively. In addition, it is shown that the blended architecture can lead to further improvements in these metrics compared to using the model-free predictors alone. The analysis also shows that there is no statistically significant difference between constant and varying delay cases in the designed experiment, nor there is any direct relation between drivers' skill level and level of improvement in metrics.

中文翻译：

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对高速遥控军用 UGV 中基于预测器的框架的评估

如果延迟得不到补偿，远程操作的无人地面车辆的机动性会在通信延迟较大的情况下受到严重影响。本文考虑了最近开发的延迟补偿理论，并提出了它在提高大延迟下高速遥控车辆的机动性和驾驶性能方面的第一个经验评估。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 所述延迟补偿理论是基于预测器的框架。考虑了该框架的两种实现：仅依赖于无模型预测器的无模型实现和混合实现，其中来自无模型预测器的航向预测与来自基于转向模型的前馈预测器的航向预测混合以更准确地预测车辆航向。在人在环仿真平台上设计了遥控跟踪任务。该平台用于比较在恒定和变化延迟下使用和不使用基于预测器的框架的遥操作性能。通过对方差的重复测量分析，得出结论：基于预测器的框架可有效实现更高的车速、更准确的横向控制和更好的驾驶性能，如赛道完成时间、赛道保持误差和赛道保持误差三个性能指标所示转向控制力，分别。此外，与单独使用无模型预测器相比，混合架构可以进一步改进这些指标。分析还表明，在设计的实验中，恒定和变化的延迟情况之间没有统计上的显着差异，司机之间也没有任何直接关系。