A Brief Survey of Telerobotic Time Delay Mitigation

Area of Science:

• Robotics and Control Systems
• Machine Learning
• Teleoperation Technology

Background:

• Teleoperation systems face significant obstacles like latency and bandwidth limitations, impacting efficacy.
• Existing solutions include control-theory and user interface designs, but newer approaches are needed.

Purpose of the Study:

• To survey the time delay problem in teleoperation.
• To focus on advanced prediction methods developed since 2014.
• To explore future research directions for time delay mitigation.

Main Methods:

• Review of control-theory-based models and user interface designs.
• Examination of time series prediction methods, including Recurrent Neural Networks, Long Short-Term Memory, Sequence to Sequence, and Generative Adversarial Networks.
• Analysis of recent advances in nonlinear statistical models, machine learning, and neural networks.

Main Results:

• Time delay remains a significant challenge in teleoperation.
• Machine learning and time series prediction offer promising avenues for addressing time delay.
• Hybrid solutions integrating user intent, robot movement, and delay prediction are anticipated.

Conclusions:

• Time delay in teleoperation is an ongoing research problem.
• Future research should explore information-theory-based modeling for novel solutions.
• Advanced machine learning techniques are crucial for improving teleoperation performance.