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<p>Human-robot collaborative digital twin (HRC-DT) systems often suffer dynamic mismatches between virtual models and their physical counterparts due to data noise, mapping latency, system instability, and external sensor disturbances. To mitigate these issues, we propose a spatiotemporal alignment framework that fuses multi-source data resampling with adaptive feedback control. First, all sampling subsystems and the digital twin platform are time-synchronized via the Network Time Protocol (NTP). We then construct a motion-confidence-driven adaptive filter (MCDAF) to suppress abrupt distortions in human skeletal data, followed by a spatial-correlation-based predictive resampling (SCPR) module that aligns sensor streams with DT update timestamps. To reduce computational and communication load while preserving the necessary human body tracking accuracy, we introduce a dynamic sampling rate proportional-derivative (PD) controller based on human joint dynamics and human-robot distance. Finally, to promptly detect any physical disturbances that the depth camera may encounter during in operation, we developed an image-based self-check module for the depth-camera offset, along with a dynamic interference guard (DIG) mechanism, and a 6D pose detection-based rapid recalibration method. Experimental results demonstrate that the proposed approach enhances dynamic consistency compared with conventional methods, and provides a robust and efficient solution for reliable HRC-DT deployment.</p>

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Real-time Spatiotemporal Synchronization for Human-robot Collaborative Systems: An Integrated Digital Twin Framework with Adaptive Data Processing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月27日

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  • Yidian Shi,
  • Liming Lao,
  • Xuan Zheng,
  • Pengzhan Chen

摘要

Human-robot collaborative digital twin (HRC-DT) systems often suffer dynamic mismatches between virtual models and their physical counterparts due to data noise, mapping latency, system instability, and external sensor disturbances. To mitigate these issues, we propose a spatiotemporal alignment framework that fuses multi-source data resampling with adaptive feedback control. First, all sampling subsystems and the digital twin platform are time-synchronized via the Network Time Protocol (NTP). We then construct a motion-confidence-driven adaptive filter (MCDAF) to suppress abrupt distortions in human skeletal data, followed by a spatial-correlation-based predictive resampling (SCPR) module that aligns sensor streams with DT update timestamps. To reduce computational and communication load while preserving the necessary human body tracking accuracy, we introduce a dynamic sampling rate proportional-derivative (PD) controller based on human joint dynamics and human-robot distance. Finally, to promptly detect any physical disturbances that the depth camera may encounter during in operation, we developed an image-based self-check module for the depth-camera offset, along with a dynamic interference guard (DIG) mechanism, and a 6D pose detection-based rapid recalibration method. Experimental results demonstrate that the proposed approach enhances dynamic consistency compared with conventional methods, and provides a robust and efficient solution for reliable HRC-DT deployment.