物理赋能的助航灯具清洗数字孪生构建

董慧芬; 孙浩远; 严力; 武云霞

doi:10.13700/j.bh.1001-5965.2022.0357

物理赋能的助航灯具清洗数字孪生构建

doi: 10.13700/j.bh.1001-5965.2022.0357

中国民航大学电子信息与自动化学院，天津 300300

基金项目: 中央高校基本科研业务费专项资金(3122022PY17)

详细信息

通讯作者:
E-mail：hfdong@cauc.edu.cn

中图分类号: V351.11；TP241.2；TP391.9
计量
- 文章访问数: 372
- HTML全文浏览量: 65
- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2022-05-16
- 录用日期: 2022-09-16
- 网络出版日期: 2022-10-09
- 整期出版日期: 2024-03-27

Digital twin construction of cleaning for navigational lamps with physical empowerment

College of Electronic Information and Automation，Civil Aviation University of China，Tianjin 300300，China

Funds: The Fundamental Research Funds for the Central Universities (3122022PY17)

More Information

Corresponding author: E-mail：hfdong@cauc.edu.cn

摘要

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。
- 数字孪生 /
- 助航灯具 /
- 机械臂 /
- 粒子模型 /
- 状态监测 /
- 同步性增强
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.
- digital twin /
- navigational lamps /
- manipulator arms /
- particle model /
- condition monitoring /
- enhanced synchronization

HTML全文

图 1 助航灯具清洗实验设备

Figure 1. Experimental equipment for cleaning navigational lamp

下载: 全尺寸图片幻灯片

图 2 数字孪生模型框架

Figure 2. Digital twin model framework

下载: 全尺寸图片幻灯片

图 3 助航灯具清洗物理和虚拟环境

Figure 3. Cleaning physical and virtual environment for navigation lamps

下载: 全尺寸图片幻灯片

图 4 虚拟环境的感知与仿真

Figure 4. Perception and simulation of virtual enviornments

下载: 全尺寸图片幻灯片

图 5 JSON解析流程

Figure 5. Parsing process of JSON

下载: 全尺寸图片幻灯片

图 6 XML解析流程

Figure 6. Parsing process of XML

下载: 全尺寸图片幻灯片

图 7 不同运动同步性增强算法效果对比

Figure 7. Effect comparison of different motion synchronization enhancement algorithms

下载: 全尺寸图片幻灯片

图 8 孪生粒子模型原理

Figure 8. Principle of twin particle model

下载: 全尺寸图片幻灯片

图 9 实际与虚拟的发光口

Figure 9. Actual and virtual luminous ports

下载: 全尺寸图片幻灯片

图 10 机械臂状态监测实验平台

Figure 10. Experimental platform for condition monitoring of robotic arm

下载: 全尺寸图片幻灯片

图 11 虚拟机械臂6个关节的位置跟踪曲线

Figure 11. Position tracking curves of six joints of a virtual robotic arm

下载: 全尺寸图片幻灯片

图 12 虚拟机械臂末端跟踪空间曲线

Figure 12. Spatial curves of virtual robot arm end tracking

下载: 全尺寸图片幻灯片

图 13 有无负载时的关节6力矩监测对比

Figure 13. Comparison of joint 6 torque monitoring with and without load

下载: 全尺寸图片幻灯片

图 14 实际的灯口清洗实验结果

Figure 14. Actual experimental results of lamp port cleaning

下载: 全尺寸图片幻灯片

图 15 孪生粒子模型模拟结果

Figure 15. Results of twin particle model simulation

下载: 全尺寸图片幻灯片

图 16 Fluent DPM仿真结果

Figure 16. Results of Fluent DPM simulation

下载: 全尺寸图片幻灯片

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
Bullet	黏性接触，碰撞边界
ODE	柔度，附着力
Vortex	滑动特性，表层厚度

下载: 导出CSV

表 2 不同数字孪生模型的性能对比

Table 2. Performance comparison of different digital twin models

模型	关节角度	末端位置	关节力矩	通信延时/ms	是否关注被执行件	虚拟环境仿真能力	数据存储
文献[13]	√	×	√		×	×	√
文献[25]	√	×	×	150	√	×	×
本文模型	√	√	√	74	√	√	×
注：“√”表示该模型具备此功能，“×”表示该模型不具备此功能。

下载: 导出CSV

参考文献(25)

[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