RoboTwin: A Robotic Teleoperation Framework Using Digital Twins

TL;DR

This paper introduces a dual digital twin framework for robotic teleoperation that reduces latency and cognitive load, improving surgical accuracy and safety in remote robotic surgeries.

Contribution

The paper presents a novel dual digital twin system that enhances teleoperation safety, accuracy, and user experience by minimizing latency and providing real-time safety layers.

Findings

Teleoperation accuracy and user experience are improved.

Network data rate for object identification is 25x lower.

Surgical quality is vastly improved with digital twins.

Abstract

Robotic surgery imposes a significant cognitive burden on the surgeon. This cognitive burden increases in the case of remote robotic surgeries due to latency between entities and thus might affect the quality of surgery. Here, the patient side and the surgeon side are geographically separated by hundreds to thousands of kilometres. Real-time teleoperation of robots requires strict latency bounds for control and feedback. We propose a dual digital twin (DT) framework and explain the simulation environment and teleoperation framework. Here, the doctor visually controls the locally available DT of the patient side and thus experiences minimum latency. The second digital twin serves two purposes. Firstly, it provides a layer of safety for operator-related mishaps, and secondly, it conveys the coordinates of known and unknown objects back to the operator's side digital twin. We show that teleoperation accuracy and user experience are enhanced with our approach. Experimental results using the NASA-TLX metric show that the quality of surgery is vastly improved with DT, perhaps due to reduced cognitive burden. The network data rate for identifying objects at the operator side is 25x lower than normal.