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

TL;DR

This study investigates how network Quality of Service impacts robotic telesurgery performance, introducing a new fault injection tool and analyzing operator performance under various network conditions.

Contribution

The paper presents NetFI, a novel fault injection tool for simulating network conditions, and provides a comprehensive analysis of network QoS effects on telesurgical task performance.

Findings

Packet loss and delay significantly impair task performance.

Certain motion primitives are more vulnerable to network degradation.

Proficiency correlates with better performance and lower workload.

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.