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量化网络延迟对遥操作机器人的影响。

Quantifying the Effects of Network Latency for a Teleoperated Robot.

作者信息

Noguera Cundar Adriana, Fotouhi Reza, Ochitwa Zachary, Obaid Haron

机构信息

Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.

Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK S7N 0W8, Canada.

出版信息

Sensors (Basel). 2023 Oct 13;23(20):8438. doi: 10.3390/s23208438.


DOI:10.3390/s23208438
PMID:37896531
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10611222/
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.

摘要

远程操作设备的发展是一个不断发展的研究领域,因为它可以提高成本效益、安全性和医疗保健的可及性。然而,由于使用远程操作设备涉及的距离较远,这些系统会出现通信质量下降的问题,如延迟或信号丢失。了解这种质量下降对于开发和提高未来系统的有效性很重要。本研究的目的是确定延迟如何影响远程操作系统的行为,并研究减轻其影响的可能方法。在本研究中,测量了一个4自由度(4-DOF)远程超声机器人的末端执行器位置误差,并将其与测量的时间延迟相关联。测试在无线局域网(WLAN)和虚拟局域网(VLAN)上进行,以监测不同网络配置下位置误差的显著变化。在本研究中,验证了主站和从站之间的通信通道是延迟的一个重要来源。此外,位置误差与延迟时间有很强的正相关。WLAN配置的平均延迟为300毫秒,最大位移误差为7.8毫米。VLAN配置显示出明显的改善,平均延迟时间减少了40%,最大位移误差减少了70%。这项工作的贡献包括量化延迟对末端执行器位置误差的影响以及不同网络配置之间的相对性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/f0394a0de2eb/sensors-23-08438-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/7e5cc25f606d/sensors-23-08438-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/674689cc88b6/sensors-23-08438-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/10a5b0355aab/sensors-23-08438-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/05a2d615ab9e/sensors-23-08438-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/3555f31335eb/sensors-23-08438-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/45ad885ce58a/sensors-23-08438-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/6551892c6919/sensors-23-08438-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/f0394a0de2eb/sensors-23-08438-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/7e5cc25f606d/sensors-23-08438-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/674689cc88b6/sensors-23-08438-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/10a5b0355aab/sensors-23-08438-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/05a2d615ab9e/sensors-23-08438-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/3555f31335eb/sensors-23-08438-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/45ad885ce58a/sensors-23-08438-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/6551892c6919/sensors-23-08438-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f9e/10611222/f0394a0de2eb/sensors-23-08438-g006.jpg

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Quantifying the Effects of Network Latency for a Teleoperated Robot.

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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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引用本文的文献

[1]
Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies.

Sensors (Basel). 2024-6-18

[2]
Design and testing of ultrasound probe adapters for a robotic imaging platform.

Sci Rep. 2024-3-1

[3]
Beyond the Scalpel: A Tapestry of Surgical Safety, Precision, and Patient Prosperity.

Cureus. 2023-12-11

[4]
Innovative Approaches to Safe Surgery: A Narrative Synthesis of Best Practices.

Cureus. 2023-11-30

本文引用的文献

[1]
Robotics and cybersurgery in ophthalmology: a current perspective.

J Robot Surg. 2023-8

[2]
Development and assessment of a telesonography system for musculoskeletal imaging.

Eur Radiol Exp. 2021-7-27

[3]
Impact of network performance on remote robotic-assisted endovascular interventions in porcine model.

J Robot Surg. 2022-2

[4]
Using conditional generative adversarial networks to reduce the effects of latency in robotic telesurgery.

J Robot Surg. 2021-8

[5]
Determination of the latency effects on surgical performance and the acceptable latency levels in telesurgery using the dV-Trainer(®) simulator.

Surg Endosc. 2014-9

[6]
Analysis of QoS Requirements for e-Health Services and Mapping to Evolved Packet System QoS Classes.

Int J Telemed Appl. 2010

[7]
Transatlantic robot-assisted telesurgery.

Nature. 2001-9-27

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