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A Comprehensive Analysis of the Effects of Network Quality of Service on Robotic Telesurgery

Zhaomeng Zhang, Seyed Hamid Reza Roodabeh, Homa Alemzadeh

TL;DR

This paper presents a comprehensive analysis of how packet loss, delay, and communication loss affect telesurgical task execution and introduces NetFI, a novel fault injection tool that emulates different network conditions using stochastic QoS models informed by real-world network data.

Abstract

The viability of long-distance telesurgery hinges on reliable network Quality of Service (QoS), yet the impact of realistic network degradations on task performance is not sufficiently understood. This paper presents a comprehensive analysis of how packet loss, delay, and communication loss affect telesurgical task execution. We introduce NetFI, a novel fault injection tool that emulates different network conditions using stochastic QoS models informed by real-world network data. By integrating NetFI with a surgical simulation platform, we conduct a user study involving 15 participants at three proficiency levels, performing a standardized Peg Transfer task under varying levels of packet loss, delay, and communication loss. We analyze the effect of network QoS on overall task performance and the fine-grained motion primitives (MPs) using objective performance and safety metrics and subjective operator's perception of workload. We identify specific MPs vulnerable to network degradation and find strong correlations between proficiency, objective performance, and subjective workload. These findings offer quantitative insights into the operational boundaries of telesurgery. Our open-source tools and annotated dataset provide a foundation for developing robust and network-aware control and mitigation strategies.

A Comprehensive Analysis of the Effects of Network Quality of Service on Robotic Telesurgery

TL;DR

This paper presents a comprehensive analysis of how packet loss, delay, and communication loss affect telesurgical task execution and introduces NetFI, a novel fault injection tool that emulates different network conditions using stochastic QoS models informed by real-world network data.

Abstract

The viability of long-distance telesurgery hinges on reliable network Quality of Service (QoS), yet the impact of realistic network degradations on task performance is not sufficiently understood. This paper presents a comprehensive analysis of how packet loss, delay, and communication loss affect telesurgical task execution. We introduce NetFI, a novel fault injection tool that emulates different network conditions using stochastic QoS models informed by real-world network data. By integrating NetFI with a surgical simulation platform, we conduct a user study involving 15 participants at three proficiency levels, performing a standardized Peg Transfer task under varying levels of packet loss, delay, and communication loss. We analyze the effect of network QoS on overall task performance and the fine-grained motion primitives (MPs) using objective performance and safety metrics and subjective operator's perception of workload. We identify specific MPs vulnerable to network degradation and find strong correlations between proficiency, objective performance, and subjective workload. These findings offer quantitative insights into the operational boundaries of telesurgery. Our open-source tools and annotated dataset provide a foundation for developing robust and network-aware control and mitigation strategies.
Paper Structure (23 sections, 6 equations, 4 figures, 4 tables)

This paper contains 23 sections, 6 equations, 4 figures, 4 tables.

Table of Contents

  1. INTRODUCTION
  2. BACKGROUND AND RELATED WORK
  3. Networks in Robotic Telesurgery
  4. Peg Transfer and Motion Primitives
  5. METHODS
  6. Teleoperation Framework
  7. NetFI QoS Emulator
  8. Models of Network Degradation
  9. Packet Loss
  10. Delay
  11. Communication Loss
  12. Emulator Parameter Optimization
  13. EXPERIMENTAL SETUP
  14. User Study Design
  15. Network Degradation Parameters and Characteristics
  16. ...and 8 more sections

Figures (4)

  • Figure 1: System Architecture of our telesurgical simulation setup with model-based network QoS emulation: the block diagram illustrates the data flow between the surgeon side console, the patient side robot simulator, and the network QoS emulator.
  • Figure 2: PT Task Workflow: Transfer of a red peg from the start pole to the goal pole in the simulation environment, modeled as a sequence of MPs, MP1 to MP9. ‘L': Left, ‘R’: Right, ‘S’: Start, and ‘G’: Goal.
  • Figure 3: Comparison of the average motion length of the PSMs and MTMs at the MP level for all participants under varying levels of packet loss, delay, and communication loss.
  • Figure 4: TLX Perceived Task Load across all participants