Predicted Trajectory Guidance Control Framework of Teleoperated Ground Vehicles Compensating for Delays

Zhang, Qiang; Xu, Zhouli; Wang, Yihang; Yang, Lizhong; Song, Xiaolin; Huang, Zhi Feng

doi:10.1109/tvt.2023.3269517

Cited by 2 publications

(4 citation statements)

References 38 publications

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“…Other similar examples include Refs. [21,22,[48][49][50][51][52][53][54][55][56][57][58][59][60][61].…”

Section: Modelling Network Delaymentioning

confidence: 99%

“…In the literature, most studies employ or combine traditional NN methods such as auto-encoder (AE), variational auto-encoder (VAE), recurrent neural network (RNN), and long short-term memory (LSTM) for time-series data [ 12 ]. Among these, RNN [ 74 , 117 ] and LSTM [ 55 , 69 , 74 , 118 , 119 ] are more suitable for time-series prediction. However, sometimes RNNs cannot learn long-term dependencies, whereas LSTMs have the advantage of retaining both long- and short-term memory.…”

Section: Latency Mitigation Strategiesmentioning

confidence: 99%

See 1 more Smart Citation

Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies

Kamtam,

Lu,

Bouali

et al. 2024

Sensors

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With remarkable advancements in the development of connected and autonomous vehicles (CAVs), the integration of teleoperation has become crucial for improving safety and operational efficiency. However, teleoperation faces substantial challenges, with network latency being a critical factor influencing its performance. This survey paper explores the impact of network latency along with state-of-the-art mitigation/compensation approaches. It examines cascading effects on teleoperation communication links (i.e., uplink and downlink) and how delays in data transmission affect the real-time perception and decision-making of operators. By elucidating the challenges and available mitigation strategies, the paper offers valuable insights for researchers, engineers, and practitioners working towards the seamless integration of teleoperation in the evolving landscape of CAVs.

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“…Other similar examples include Refs. [21,22,[48][49][50][51][52][53][54][55][56][57][58][59][60][61].…”

Section: Modelling Network Delaymentioning

confidence: 99%

Section: Latency Mitigation Strategiesmentioning

confidence: 99%

Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies

Kamtam,

Lu,

Bouali

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Incorporating OFC principles can lead to the development of teleoperated systems that better compensate for these delays by adjusting the control algorithms to anticipate and mitigate the impact of latency on motor accuracy (Mitrovic et al, 2010 ; Zhu et al, 2023 ). Studies such as Franklin and Wolpert ( 2011 ) have demonstrated that applying OFC in robotic systems enables more adaptive and resilient responses to unexpected changes or errors in movement execution, enhancing the overall effectiveness of teleoperated tasks (Zhang et al, 2023 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Simulated feedback, when designed effectively, could harness these predictive mechanisms, potentially reducing the cognitive load and improving motor execution in teleoperation scenarios. The SMA and pre-SMA, regions involved in the initiation and temporal organization of movements (Shima and Tanji, 1998 ; Zhang et al, 2023 ), may also benefit from simulated feedback. By providing early sensory cues, simulated feedback could help in “pre-setting” these regions, allowing for more accurate timing predictions and motor responses despite delays (Kilavik et al, 2014 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Neural dynamics of delayed feedback in robot teleoperation: insights from fNIRS analysis

Zhou,

Ye,

Zhu

et al. 2024

Front. Hum. Neurosci.

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IntroductionAs robot teleoperation increasingly becomes integral in executing tasks in distant, hazardous, or inaccessible environments, operational delays remain a significant obstacle. These delays, inherent in signal transmission and processing, adversely affect operator performance, particularly in tasks requiring precision and timeliness. While current research has made strides in mitigating these delays through advanced control strategies and training methods, a crucial gap persists in understanding the neurofunctional impacts of these delays and the efficacy of countermeasures from a cognitive perspective.MethodsThis study addresses the gap by leveraging functional Near-Infrared Spectroscopy (fNIRS) to examine the neurofunctional implications of simulated haptic feedback on cognitive activity and motor coordination under delayed conditions. In a human-subject experiment (N = 41), sensory feedback was manipulated to observe its influences on various brain regions of interest (ROIs) during teleoperation tasks. The fNIRS data provided a detailed assessment of cerebral activity, particularly in ROIs implicated in time perception and the execution of precise movements.ResultsOur results reveal that the anchoring condition, which provided immediate simulated haptic feedback with a delayed visual cue, significantly optimized neural functions related to time perception and motor coordination. This condition also improved motor performance compared to the asynchronous condition, where visual and haptic feedback were misaligned.DiscussionThese findings provide empirical evidence about the neurofunctional basis of the enhanced motor performance with simulated synthetic force feedback in the presence of teleoperation delays. The study highlights the potential for immediate haptic feedback to mitigate the adverse effects of operational delays, thereby improving the efficacy of teleoperation in critical applications.

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Predicted Trajectory Guidance Control Framework of Teleoperated Ground Vehicles Compensating for Delays

Cited by 2 publications

References 38 publications

Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies

Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies

Neural dynamics of delayed feedback in robot teleoperation: insights from fNIRS analysis

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