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

Zhaomeng Zhang; Seyed Hamid Reza Roodabeh; Homa Alemzadeh

cs.ROMar 6, 2026

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

Authors: Zhaomeng Zhang, Seyed Hamid Reza Roodabeh, Homa Alemzadeh

Organizations: Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22093, USA

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.

Figures & tables

Fig. 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.

Fig. 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.

Degradation Type	Severity	Target	Model Parameters $\theta$
Packet Loss	PLM-1	10% Loss	$\alpha_{0}=4.43,\lambda_{0}=1.64$ ; $\alpha_{1}=4.97,\lambda_{1}=0.27$
	PLM-2	30% Loss	$\alpha_{0}=4.60,\lambda_{0}=3.55$ ; $\alpha_{1}=3.90,\lambda_{1}=1.47$
	PLM-3	50% Loss	$\alpha_{0}=3.86,\lambda_{0}=3.32$ ; $\alpha_{1}=3.80,\lambda_{1}=6.05$
Delay	DLM-1	100 ms	$D_{\text{min}}=70,w=[.73,.27],\lambda=[.027,.085]$
	DLM-2	300 ms	$D_{\text{min}}=210,w=[.892,.108],\lambda=[.011,.016]$
	DLM-3	500 ms	$D_{\text{min}}=406,w=[.895,.105],\lambda=[.010,.015]$

TABLE I : Key parameters for emulated network degradations

Conditions	Transfer	MP1	MP2	MP3	MP4	MP5	MP6	MP7	MP8	MP9
Normal	22.1 ( $\pm$ 5.6)	6.0 ( $\pm$ 2.1)	1.3 ( $\pm$ 0.5)	1.6 ( $\pm$ 0.4)	5.6 ( $\pm$ 1.7)	1.3 ( $\pm$ 0.4)	0.9 ( $\pm$ 0.3)	0.8 ( $\pm$ 0.2)	3.5 ( $\pm$ 1.1)	1.2 ( $\pm$ 0.4)
PLM-1	24.4 ( $\pm$ 8.3)	7.5 ( $\pm$ 4.0)	1.2 ( $\pm$ 0.5)	1.9 ( $\pm$ 0.9)	6.4 ( $\pm$ 3.4)	1.3 ( $\pm$ 0.4)	0.9 ( $\pm$ 0.3)	0.8 ( $\pm$ 0.3)	3.7 ( $\pm$ 1.6)	1.1 ( $\pm$ 0.3)
PLM-2	26.6 ( $\pm$ 7.4)	6.9 ( $\pm$ 2.9)	1.3 ( $\pm$ 0.7)	1.9 ( $\pm$ 0.8)	7.9 ( $\pm$ 3.2)	1.3 ( $\pm$ 0.6)	1.0 ( $\pm$ 0.4)	0.9 ( $\pm$ 0.3)	4.1 ( $\pm$ 2.0)	1.1 ( $\pm$ 0.3)
PLM-3	36.2 ∗ ( $\pm$ 10.7)	10.7 ∗ ( $\pm$ 5.0)	1.3 ( $\pm$ 0.5)	2.8 ( $\pm$ 1.4)	9.8 ∗ ( $\pm$ 3.7)	1.6 ( $\pm$ 0.6)	1.3 ( $\pm$ 0.6)	1.2 ( $\pm$ 0.4)	5.6 ∗ ( $\pm$ 1.6)	1.1 ( $\pm$ 0.2)
DLM-1	27.1 ( $\pm$ 9.7)	7.0 ( $\pm$ 3.1)	1.2 ( $\pm$ 0.4)	1.8 ( $\pm$ 0.8)	7.4 ( $\pm$ 4.0)	1.4 ( $\pm$ 0.6)	1.0 ( $\pm$ 0.4)	1.0 ( $\pm$ 0.3)	4.5 ( $\pm$ 1.8)	1.3 ( $\pm$ 0.5)
DLM-2	34.1 ∗ ( $\pm$ 6.5)	10.4 ∗ ( $\pm$ 3.9)	1.6 ( $\pm$ 1.1)	2.5 ( $\pm$ 0.9)	8.8 ∗ ( $\pm$ 2.9)	1.6 ( $\pm$ 0.6)	1.2 ( $\pm$ 0.4)	1.4 ( $\pm$ 0.5)	6.1 ∗ ( $\pm$ 1.9)	1.6 ( $\pm$ 0.6)

TABLE II : Mean and standard deviation of Completion Time (s) for single peg transfer and individual MPs under network degradations across participants (bold values indicate changes exceeding 1.5s in MPs and 5s in the overall transfer from normal; * indicates a statistically significant difference between the normal condition and each network degradation based on a paired t-test

(p<0.001)

)

Fig. 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.

Conditions	All	Novice	Intermediate	Expert
Normal	9 ( $\pm$ 4.4)	14 ( $\pm$ 1.7)	8 ( $\pm$ 0.8)	4 ( $\pm$ 1.0)
PLM-1	9 ( $\pm$ 6.9)	13 ( $\pm$ 9.7)	10 ( $\pm$ 3.3)	4 ( $\pm$ 1.7)
PLM-2	13 ( $\pm$ 6.6)	16 ( $\pm$ 5.0)	16 ( $\pm$ 6.3)	6 ( $\pm$ 2.3)
PLM-3	23 ( $\pm$ 14.2)	36 ( $\pm$ 16.9)	22 ( $\pm$ 5.3)	12 ( $\pm$ 1.3)
DLM-1	8 ( $\pm$ 5.3)	14 ( $\pm$ 3.7)	8 ( $\pm$ 3.7)	3 ( $\pm$ 1.0)
DLM-2	10 ( $\pm$ 5.3)	15 ( $\pm$ 6.1)	9 ( $\pm$ 2.1)	6 ( $\pm$ 2.2)

TABLE III : Mean and standard deviation of clutch usage for the six-peg transfer of all participants and each proficiency group (bold values indicate the highest value within each group and arrows denote distinct trends relative to the preceding severity level)

Conditions	Success Rate	Error Counts				Failure Counts
	(%)	(1)	(2)	(3)	(4)	(1)	(2)	(3)
Normal-1	100.0 (90/90)	8	3	0	0	0	0	0
Normal-2	100.0 (90/90)	9	6	3	0	0	0	0
Normal-3	98.8 (86/87)	4	6	2	0	1	0	0
PLM-1	93.2 (82/88)	11	12	5	0	6	0	0
PLM-2	97.8 (88/90)	15	5	7	0	1	1	0

TABLE IV : Success Rate, Error Counts, and Failure Counts. Error Modes : (1) Multiple Attempts; (2) Object Drop; (3) Collision; (4) Out of View; Failure Modes : (1) On-Board Drop; (2) Off-Board Drop; (3) Incorrect Placement (rows for the most error-prone network degradation levels are in bold)

Fig. 4 : TLX Perceived Task Load across all participants

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Real-time video streaming is crucial in surgical teleoperation, yet reproducible evaluation under realistic network impairments remains limited. This paper presents VISTA, a benchmark designed to study how impairments along the forward video path affect received video quality, temporal continuity, and human task performance. VISTA employs Linux Traffic Control with NetEm and a Gilbert-Elliott loss model to emulate five network conditions: Hospital LAN, 5G Urban, 4G Rural, LEO Satellite, and GEO Satellite. The benchmark integrates a standardised peg transfer task with synchronized measurements of network quality of service (QoS), objective video quality (PSNR, SSIM, and VMAF), and temporal continuity through freeze rate, while maintaining a stable reverse control channel. Across 375 experimental trials, network degradation substantially reduced teleoperation performance: success rate decreased from 97% in Hospital LAN to 79% in 5G Urban, 35% in 4G Rural, 71% in LEO Satellite, and 12% in GEO Satellite, while mean task completion time for successful trials increased from 80 s in Hospital LAN to 117 s in 5G Urban, 211 s in 4G Rural, 152 s in LEO Satellite, and 255 s in GEO Satellite. These findings show that network impairments have a direct impact on task completion and success in surgical teleoperation, and provide a reproducible basis for evaluating teleoperation video under realistic network constraints. Source code available at https://github.com/Dzxx623/VISTA.

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