煤田地质与勘探2025，Vol.53Issue(5)：228-238,11.DOI:10.12363/issn.1001-1986.25.03.0154
面间煤柱掘支机器人集群数字孪生系统高效虚实同步方法
A method for efficient virtual-physical synchronization of the digital twin system of an excavation-supporting robot cluster targeting coal pillars between mining faces
摘要
Abstract
[Objective]Virtual models for excavation-supporting robot clusters targeting coal pillars between mining faces encounter challenges like a large data size and anomalies in data transmission,which lead to poor virtual-physical synchronization.This study proposed a method for efficient virtual-physical synchronization of the digital twin(DT)system of an excavation-supporting robot cluster using 3D model lightweighting and a trajectory prediction and correc-tion model.[Methods]Fit-controlling vertices were defined,and their collapse cost factor was introduced to improve the quadratic error metric(QEM)algorithm and to constrain the lightweighting process of the 3D model of an assembly while maintaining fits between components.This leads to a decreased data size.A trajectory prediction-correction mod-el was developed for the excavation-supporting robot cluster.Specifically,the movement trajectories of the twin robot cluster were predicted using the self-attention-long short-term memory(LSTM)-based trajectory prediction algorithm,followed by the real-time correction of the predicted trajectories using quadratic interpolation.This helps ensure the spa-tiotemporal consistency of the synchronization between the virtual model and the physical equipment.Furthermore,a simulation platform was constructed for DT-based efficient virtual-physical synchronization of an excavation-support-ing robot cluster.[Results and Conclusions]The results indicate that the lightweighting process under the constraint of the collapse cost factor of fit-controlling vertices effectively suppressed the geometric error propagation while maintain-ing the mating surfaces in the assembly roughly unchanged,achieving a data compression ratio of 90%.For the predic-tion of the movement trajectories within 1.5 s,the self-attention-LSTM-based prediction algorithm yielded the lowest er-rors.The trajectory prediction-correction method reduced the mean absolute deviation(MAD)of the driving trajectory by 74.28%,effectively ensuring consistent,stable virtual-physical synchronization.The results indicate a maximum vir-tual-physical synchronization latency of 55.28 ms,an absolute positional error of 1.93 mm,and a relative positional er-ror of 1.07%,suggesting high-accuracy,low-latency virtual-physical synchronization of an excavation-supporting robot cluster.The proposed method provides a new philosophy for enhancing the operational efficiency of the DT system of coal mining equipment.关键词
面间煤柱/数字孪生/掘支机器人集群/模型轻量化/二次误差度量/轨迹预测-修正Key words
coal pillar between mining faces/digital twin(DT)/excavation-supporting robot cluster/model lightweight-ing/quadratic error metric(QEM)/trajectory prediction-correction分类
矿山工程引用本文复制引用
毛清华,司马俊雷,马宏伟,王川伟,陈彦璋,郭文瑾,崔闻达,成佳帅..面间煤柱掘支机器人集群数字孪生系统高效虚实同步方法[J].煤田地质与勘探,2025,53(5):228-238,11.基金项目
国家自然科学基金项目(52174150) （52174150）
陕西省重点研发计划项目(2023-LL-QY-03) （2023-LL-QY-03）
国家重点研发计划项目(2023YFC2907600) （2023YFC2907600）
陕西高校青年创新团队项目(2022TD-043) （2022TD-043）
