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Towards 5G Enabled Tactile Robotic Telesurgery

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arxiv 1803.03586 v1 pith:UOKJYMGQ submitted 2018-03-09 cs.NI

Qi Zhang , Jianhui Liu , Guodong Zhao This is my paper · ORCID
classification cs.NI
keywords robotictelesurgerycommunicationsystemchallengesenablednetworkperformance
verification ladder T0 review T1 audit T2 compute T3 formal

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Robotic telesurgery has a potential to provide extreme and urgent health care services and bring unprecedented opportunities to deliver highly specialized skills globally. It has a significant societal impact and is regarded as one of the appealing use cases of Tactile Internet and 5G applications. However, the performance of robotic telesurgery largely depends on the network performance in terms of latency, jitter and packet loss, especially when telesurgical system is equipped with haptic feedback. This imposes significant challenges to design a reliable and secure but cost-effective communication solution. This article aims to give a better understanding of the characteristics of robotic telesurgical system, and the limiting factors, the possible telesurgery services and the communication quality of service (QoS) requirements of the multi-modal sensory data. Based on this, a viable network architecture enabled by the converged edge and core cloud is presented and the relevant research challenges, open issues and enabling technologies in the 5G communication system are discussed.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model

    cs.RO 2025-01 reject novelty 4.0 of 10

    An Informer transformer predicts surgical tool-tip position under simulated packet loss and is reported to exceed 90% accuracy on JIGSAWS knot-tying trials.

  2. Signal Prediction for Loss Mitigation in Tactile Internet: A Leader-Follower Game-Theoretic Approach

    eess.SP 2025-07 reject novelty 3.0 of 10

    A leader-follower game framing with two neural networks predicts next-step haptic signals, reporting 80-95% human-side and 70-90% robot-side accuracy, with a Taylor loss bound presented as a robustness guarantee.

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