Digital twin construction of cleaning for navigational lamps with physical empowerment
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摘要:
机场助航灯具清洗设备工作时需要远程实时监测设备状态和清洗质量。数字孪生通过虚拟平台映射清洗设备的工作情况,而目前虚拟环境的建立仍大量依赖于数据驱动,自身缺乏感知进化和虚拟仿真的能力,且对被执行件的关注较少。针对以上问题,提出基于多物理引擎的数字孪生模型。该系统利用CoppeliaSim构建数据和脚本双驱动的虚拟空间,融合了虚拟传感和视觉检测,并通过BlueZero实现实时通信。为解决通信延迟造成的数据流阶梯跳跃的问题,提出一种基于改进均值滤波的运动同步性增强算法,内嵌在Qt建立的数据集成子系统中。实验结果表明:经运动增强后,所提系统的虚实同步误差为74 ms,满足同步性要求;清洗机械臂关节角度跟踪的均方误差为0.827°,末端空间位置跟踪误差不超过2.775 mm,满足跟踪精度要求;所提模型能够动态呈现灯具被清洗时的污斑状况,证明了所提模型的合理性,满足助航灯具清洗过程的应用需求。
Abstract:Airport navigational lamp cleaning equipment needs remote real-time monitoring of equipment condition and cleaning quality. Virtual platforms are utilized to map the operating conditions of the cleaning equipment. The establishment of the virtual environment is still largely dependent on data-driven, lacking the ability to perceive evolution and virtual simulation, and paying less attention to the executed parts. In view of the above problems, a digital twin model based on the multi-physical engine is proposed. The system uses CoppeliaSim to build a data and script dual-driven virtual space, integrates virtual sensing and visual detection, and realizes real-time communication through BlueZero. We present a motion enhancement technique based on improved mean filtering, which is implemented in the data integration subsystem provided by Qt, to address the issue of data flow ladder jump caused by communication delay.The experimental results show that the virtual-real synchronization error of the model is 74 ms after motion enhancement, which meets the synchronization requirements. The mean square error of the joint angle tracking of the cleaning manipulator is 0.827°, and the tracking error of the spatial position of the end is less than 2.775 mm, which meets the tracking accuracy requirements. The proposed model can dynamically present the stain condition when the lamps are cleaned, which proves the rationality of the proposed model and meets the application requirements of the cleaning process of the navigation lamps.
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Key words:
- digital twin /
- navigational lamps /
- manipulator arms /
- particle model /
- condition monitoring /
- enhanced synchronization
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表 1 部分引擎的可调参数
Table 1. Adjustable parameters of some engines
物理引擎 属性 Bullet 摩擦系数,线性阻尼,角阻尼 黏性接触,碰撞边界 ODE 柔度,附着力 Vortex 滑动特性,表层厚度 -
[1] GRIEVES M, VICKERS J. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems[M]//KAHLEN F J, FLUMERFELT S, ALVES A. Transdisciplinary Perspectives on Complex Systems. Berlin: Springer, 2017: 85-113. [2] 陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统, 2019, 25(1): 1-18.TAO F, LIU W R, ZHANG M, et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems, 2019, 25(1): 1-18(in Chinese). [3] TAO F, SUI F Y, LIU A, et al. Digital twin-driven product design framework[J]. International Journal of Production Research, 2019, 57(12): 3935-3953. doi: 10.1080/00207543.2018.1443229 [4] 陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24(1): 1-18.TAO F, LIU W R, LIU J H, et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1): 1-18(in Chinese). [5] REDELINGHUYS A J H, BASSON A H, KRUGER K. A six-layer architecture for the digital twin: A manufacturing case study implementation[J]. Journal of Intelligent Manufacturing, 2020, 31(6): 1383-1402. doi: 10.1007/s10845-019-01516-6 [6] JONES D, SNIDER C, NASSEHI A, et al. Characterising the digital twin: A systematic literature review[J]. CIRP Journal of Manufacturing Science and Technology, 2020, 29: 36-52. doi: 10.1016/j.cirpj.2020.02.002 [7] NEWRZELLA S R, FRANKLIN D W, HAIDER S. 5-dimension cross-industry digital twin applications model and analysis of digital twin classification terms and models[J]. IEEE Access, 2021, 9: 131306-131321. doi: 10.1109/ACCESS.2021.3115055 [8] WARD R, SUN C, DOMINGUEZ-CABALLERO J, et al. Machining digital twin using real-time model-based simulations and lookahead function for closed loop machining control[J]. The International Journal of Advanced Manufacturing Technology, 2021, 117(11): 3615-3629. [9] XU W J, CUI J, LI L, et al. Digital twin-based industrial cloud robotics: Framework, control approach and implementation[J]. Journal of Manufacturing Systems, 2021, 58: 196-209. doi: 10.1016/j.jmsy.2020.07.013 [10] ZHENG Y, YANG S, CHENG H C. An application framework of digital twin and its case study[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(3): 1141-1153. doi: 10.1007/s12652-018-0911-3 [11] 孙惠斌, 潘军林, 张纪铎, 等. 面向切削过程的刀具数字孪生模型[J]. 计算机集成制造系统, 2019, 25(6): 1474-1480.SUN H B, PAN J L, ZHANG J D, et al. Digital twin model for cutting tools in machining process[J]. Computer Integrated Manufacturing Systems, 2019, 25(6): 1474-1480(in Chinese). [12] XIE J M. Research on key technologies base Unity3D game engine[C]//Proceedings of the 7th International Conference on Computer Science & Education. Piscataway: IEEE Press, 2012: 695-699. [13] 杜莹莹, 罗映, 彭义兵, 等. 基于数字孪生的工业机器人三维可视化监控[J]. 计算机集成制造系统, 2023, 29(6): 2130-2138.DU Y Y, LUO Y, PENG Y B, et al. 3D visual monitoring system of industrial robot based on digital twin[J]. Computer Integrated Manufacturing Systems, 2023, 29(6): 2130-2138(in Chinese). [14] IGAWA N, YOKOGAWA T, TAKAHASHI M, et al. Model checking of visual scripts created by UE4 blueprints[C]//Proceedings of the 9th International Congress on Advanced Applied Informatics. Piscataway: IEEE Press, 2021: 512-515. [15] ZHANG X Y, GRAČANIN D. An approach to WebGL based distributed virtual environments[C]//Proceedings of the 18th International Conference on 3D Web Technology. New York: ACM, 2013: 195-198. [16] REGUEIRO M A, VIQUEIRA J R R, TABOADA J A, et al. Virtual integration of sensor observation data[J]. Computers & Geosciences, 2015, 81(8): 12-19. [17] VALDÉS J J. Extreme learning machines with heterogeneous data types[J]. Neurocomputing, 2018, 277: 38-52. doi: 10.1016/j.neucom.2017.02.103 [18] ALA-LAURINAHO R, AUTIOSALO J, NIKANDER A, et al. Data link for the creation of digital twins[J]. IEEE Access, 2020, 8: 228675-228684. doi: 10.1109/ACCESS.2020.3045856 [19] WANG F, HU L, ZHOU J, et al. A semantics-based approach to multi-source heterogeneous information fusion in the Internet of Things[J]. Soft Computing, 2017, 21(8): 2005-2013. doi: 10.1007/s00500-015-1899-7 [20] AL-BALTAH I A, GHANI A A A, AL-GOMAEI G M, et al. A scalable semantic data fusion framework for heterogeneous sensors data[J]. Journal of Ambient Intelligence and Humanized Computing, 2023, 14(5): 5047-5066. doi: 10.1007/s12652-020-02527-5 [21] FUSCO G, AVERSANO L. An approach for semantic integration of heterogeneous data sources[J]. PeerJ Computer Science, 2020, 6(2): 254. doi: 10.7717/peerj-cs.254 [22] LIU S M, BAO J S, LU Y Q, et al. Digital twin modeling method based on biomimicry for machining aerospace components[J]. Journal of Manufacturing Systems, 2021, 58: 180-195. doi: 10.1016/j.jmsy.2020.04.014 [23] LEE X Y, GAO G W. An improved CI EKF data fusion algorithm for multi-sensor time-delay system[J]. IOP Conference Series:Materials Science and Engineering, 2019, 631(5): 052020. doi: 10.1088/1757-899X/631/5/052020 [24] 张政, 谢灼利. 流体-固体两相流的数值模拟[J]. 化工学报, 2001, 52(1): 1-12.ZHANG Z, XIE Z L. Numerical simulation of fluid-solid two-phase flows[J]. Journal of Chemical Industry and Engineering, 2001, 52(1): 1-12(in Chinese). [25] LAAKI H, MICHE Y, TAMMI K. Prototyping a digital twin for real time remote control over mobile networks: Application of remote surgery[J]. IEEE Access, 2019, 7: 20325-20336. doi: 10.1109/ACCESS.2019.2897018