Quantifying the Effects of Network Latency for a Teleoperated Robot
Bibliographic record
Abstract
The development of teleoperated devices is a growing area of study since it can improve cost effectiveness, safety, and healthcare accessibility. However, due to the large distances involved in using teleoperated devices, these systems suffer from communication degradation, such as latency or signal loss. Understanding degradation is important to develop and improve the effectiveness of future systems. The objective of this research is to identify how a teleoperated system's behavior is affected by latency and to investigate possible methods to mitigate its effects. In this research, the end-effector position error of a 4-degree-of-freedom (4-DOF) teleultrasound robot was measured and correlated with measured time delay. The tests were conducted on a Wireless Local Area Network (WLAN) and a Virtual Local Area Network (VLAN) to monitor noticeable changes in position error with different network configurations. In this study, it was verified that the communication channel between master and slave stations was a significant source of delay. In addition, position error had a strong positive correlation with delay time. The WLAN configuration achieved an average of 300 ms of delay and a maximum displacement error of 7.8 mm. The VLAN configuration showed a noticeable improvement with a 40% decrease in average delay time and a 70% decrease in maximum displacement error. The contribution of this work includes quantifying the effects of delay on end-effector position error and the relative performance between different network configurations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".