TeleSim构建网络感知测试平台，评估远程操作在不同网络条件下的表现。
TeleSim: A Network-Aware Testbed and Benchmark Dataset for Telerobotic Applications
- 基于OMNeT++模拟三类网络质量，控制带宽、延迟等参数
- 最差网络下任务耗时增221.8%，成功率降64%
- 适合研究远程手术、核设施作业等高可靠性需求场景
远程操控技术在远程手术、核电退役和太空探索等领域日益重要。可靠的测试数据集与实验平台对部署前评估系统性能至关重要。然而，现有数据集普遍缺乏对网络延迟影响的捕捉，且缺乏真实模拟操作者与机器人通信链路的测试平台。本文提出TeleSim，一个面向远程操控应用的网络感知型数据集与测试平台，用于在多种网络条件下评估系统表现。通过精确控制带宽、延迟、抖动和丢包率，系统性地采集了300次精细操作任务的数据，涵盖完成时间、成功率、视频质量指标（峰值信噪比PSNR与结构相似性SSIM）及服务质量QoS参数。在最差网络条件下，任务完成时间增加221.8%，成功率下降64%。结果表明，网络退化引发多重负面效应，显著降低视频质量并延长任务执行时间，凸显自适应、鲁棒远程操控协议的必要性。完整数据集与测试平台代码已公开于GitHub：https://github.com/ConnectedRoboticsLab 及YouTube视频演示：https://youtu.be/Fz_1iOYe104。
原文摘要 · Abstract (English)
Telerobotic technologies are becoming increasingly essential in fields such as remote surgery, nuclear decommissioning, and space exploration. Reliable datasets and testbeds are essential for evaluating telerobotic system performance prior to real-world deployment. However, there is a notable lack of datasets that capture the impact of network delays, as well as testbeds that realistically model the communication link between the operator and the robot. This paper introduces TeleSim, a network-aware teleoperation dataset and testbed designed to assess the performance of telerobotic applications under diverse network conditions. TeleSim systematically collects performance data from fine manipulation tasks executed under three predefined network quality tiers: High, Medium, and Low. Each tier is characterized through controlled settings of bandwidth, latency, jitter, and packet loss. Using OMNeT++ for precise network simulation, we record a wide range of metrics, including completion time, success rates, video quality indicators (Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM)), and quality of service (QoS) parameters. TeleSim comprises 300 experimental trials, providing a robust benchmark for evaluating teleoperation systems across heterogeneous network scenarios. In the worst network condition, completion time increases by 221.8% and success rate drops by 64%. Our findings reveal that network degradation leads to compounding negative impacts, notably reduced video quality and prolonged task execution, highlighting the need for adaptive, resilient teleoperation protocols. The full dataset and testbed software are publicly available on our GitHub repository: https://github.com/ConnectedRoboticsLab and YouTube channel: https://youtu.be/Fz_1iOYe104.
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