Network Delay Forecast and Master–Slave Consistency Enhancement for Remote Surgical Robots

Background

The inevitable network delay can directly impact the process of remote surgeries and affect the master–slave motion consistency, and sudden changes in delay can compromise surgical safety.

Methods

Firstly, real-time calibration of unidirectional network delays is performed. Subsequently, the network delay is forecasted with a real-time training parallel recurrent neural network for safety warnings, and the real-time forecast of slave manipulator position is performed to enhanced the master–slave motion consistency. Finally, the forecast accuracy across multiple scales is assessed to provide feedback.

Results

The programme can operate on standard computers at distances of at least 630 km. Our forecast method meets the real-time requirement, demonstrates strong generalisation capabilities and reduces the impact of network delay on master–slave motion consistency to approximately 20%–80% of its original level.

Conclusions

The proposed forecast method enables real-time delay forecast for remote surgeries, reducing the impact of delay on master–slave motion consistency.