Evaluation of a Predictor-Based Framework in High-Speed Teleoperated Military UGVs

• Published in: IEEE transactions on human-machine systems 2020-12, Vol.50 (6), p.561-572
• Main Authors: Zheng, Yingshi, Brudnak, Mark J., Jayakumar, Paramsothy, Stein, Jeffrey L., Ersal, Tulga
• Format: Article
• Language: English
• Subjects: Compensation; Completion time; Delay; Delay compensation; Empirical analysis; High speed; Human in the loop; human-in-the-loop experiment; Land vehicles; Lateral control; Military vehicles; Performance measurement; Predictive models; predictor-based framework; Simulation; Steering; teleoperation; Track keeping; Traffic speed; Unmanned ground vehicles; unmanned ground vehicles (UGVs); Unmanned vehicles; Variance analysis
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2020.3018684

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.