Mtowe Daniel Poul, Long Lika, Kim Dong Min
Department of ICT Convergence, Graduate School, Soonchunhyang University, Asan 31538, Republic of Korea.
Department of Internet of Things, Soonchunhyang University, Asan 31538, Republic of Korea.
Sensors (Basel). 2025 Jul 28;25(15):4666. doi: 10.3390/s25154666.
This paper proposes a low-latency and scalable architecture for Edge-Enabled Digital Twin networked control systems (E-DTNCS) aimed at multi-robot collision avoidance and remote control in dynamic and latency-sensitive environments. Traditional approaches, which rely on centralized cloud processing or direct sensor-to-controller communication, are inherently limited by excessive network latency, bandwidth bottlenecks, and a lack of predictive decision-making, thus constraining their effectiveness in real-time multi-agent systems. To overcome these limitations, we propose a novel framework that seamlessly integrates edge computing with digital twin (DT) technology. By performing localized preprocessing at the edge, the system extracts semantically rich features from raw sensor data streams, reducing the transmission overhead of the original data. This shift from raw data to feature-based communication significantly alleviates network congestion and enhances system responsiveness. The DT layer leverages these extracted features to maintain high-fidelity synchronization with physical robots and to execute predictive models for proactive collision avoidance. To empirically validate the framework, a real-world testbed was developed, and extensive experiments were conducted with multiple mobile robots. The results revealed a substantial reduction in collision rates when DT was deployed, and further improvements were observed with E-DTNCS integration due to significantly reduced latency. These findings confirm the system's enhanced responsiveness and its effectiveness in handling real-time control tasks. The proposed framework demonstrates the potential of combining edge intelligence with DT-driven control in advancing the reliability, scalability, and real-time performance of multi-robot systems for industrial automation and mission-critical cyber-physical applications.
本文针对动态且对延迟敏感的环境中的多机器人避碰和远程控制，提出了一种用于支持边缘的数字孪生网络控制系统（E-DTNCS）的低延迟且可扩展的架构。传统方法依赖于集中式云处理或传感器到控制器的直接通信，本质上受到过多网络延迟、带宽瓶颈以及缺乏预测性决策的限制，从而限制了它们在实时多智能体系统中的有效性。为了克服这些限制，我们提出了一种将边缘计算与数字孪生（DT）技术无缝集成的新颖框架。通过在边缘执行局部预处理，系统从原始传感器数据流中提取语义丰富的特征，减少了原始数据的传输开销。这种从原始数据到基于特征的通信的转变显著缓解了网络拥塞并增强了系统响应能力。DT层利用这些提取的特征与物理机器人保持高保真同步，并执行预测模型以实现主动避碰。为了通过实验验证该框架，开发了一个真实世界的测试平台，并使用多个移动机器人进行了广泛的实验。结果表明，部署DT时碰撞率大幅降低，并且由于延迟显著降低，在E-DTNCS集成中观察到了进一步的改进。这些发现证实了该系统在处理实时控制任务方面增强的响应能力及其有效性。所提出的框架展示了将边缘智能与DT驱动的控制相结合在提高用于工业自动化和关键任务网络物理应用的多机器人系统的可靠性、可扩展性和实时性能方面的潜力。
