数字孪生驱动的巷道自动成形截割虚拟调试方法研究

张旭辉; 刘彦徽; 杨文娟; 张超; 杜昱阳; 杨骏豪; 杨雯雨

doi:10.13272/j.issn.1671-251x.18186

数字孪生驱动的巷道自动成形截割虚拟调试方法研究

doi: 10.13272/j.issn.1671-251x.18186

张旭辉^{1, 2,},
刘彦徽¹,
杨文娟^{1, 2, ,},
张超¹,
杜昱阳¹,
杨骏豪¹,
杨雯雨¹

1.
西安科技大学机械工程学院，陕西西安　710054
2.
西安科技大学陕西省矿山机电装备智能检测与控制重点实验室，陕西西安　710054

基金项目: 国家自然科学基金青年项目(52104166)。

详细信息

作者简介:
张旭辉（1972—），男，陕西凤翔人，教授，博士，研究方向为煤矿机电设备智能检测与控制，E-mail：zhangxh@xust.edu.cn

通讯作者:
杨文娟（1989—），女，山西文水人，副教授，研究方向为智能检测与控制，E-mail： yangwenjuan@xust.edu.cn。

中图分类号: TD67
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出版历程
- 收稿日期: 2024-04-09
- 修回日期: 2024-06-25
- 网络出版日期: 2024-07-30

Research on a digital twin driven virtual debugging method for roadway automatic forming cutting

1.
College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China
2.
Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi'an University of Science and Technology, Xi'an 710054, China

摘要

摘要: 针对目前巷道自动成形截割控制调试周期长、调试成本大、安全风险大、成形质量难以评价等问题，提出了一种数字孪生驱动的巷道自动成形截割虚拟调试方法。采用基于即时外观建图（RTAP−MAP）技术重建巷道三维环境，构建掘进机控制系统模型，形成虚拟调试环境，并利用虚拟传感器技术实现物理空间到虚拟环境状态的精准映射。针对难以量化评估断面成形质量的问题，确立了巷道自动成形截割性能评价方法，以断面成形截割控制过程在数据传输中心的记录为基础，主要对断面成形精度、截割效率与油缸开关次数、硬岩切割调整、超挖欠挖4个评价指标进行计算，从而为深度学习算法的迭代优化提供精准反馈信号，并提出了一种融合强化学习的自动截割控制策略，以提高自动化作业的适应性和精确度。为验证该虚拟调试方法的有效性和准确性，搭建了掘进机自动控制实验平台，并将虚拟调试系统应用于掘进巷道成形截割自动控制程序中。虚拟仿真结果表明：① 被调试软件在控制关键点位处的X，Y，Z轴定位误差的最大值分别为74.8，72.93，123.67 mm，说明虚拟调试方法的定位精度达到性能要求。② 虚拟样机与物理样机轨迹基本一致，说明该调试方法实现了对物理空间的映射。应用结果表明：① 强化学习控制器在虚拟掘进测试中适应了复杂环境，将虚拟传感器输入有效转换为精准控制指令，验证了模拟−现实迁移训练的可行性。通过处理掘进精度和避免超欠挖的实时反馈，控制器学习并优化了策略。② 优化后的断面成形截割控制性能得到了提升，根据数据库中控制量时间戳的记录，用时126 s，较优化前耗时减少了8 s。③ 优化后截割部末端轨迹跟踪最大误差为6.0 mm，较优化前降低了0.3 mm，避免了截割轨迹抖动导致的欠挖，同时使得轨迹和断面更加平滑。
- 巷道自动成形截割 /
- 数字孪生 /
- 虚拟调试 /
- 虚拟传感器 /
- 虚实坐标转换
Abstract: In response to the problems of long debugging cycle, high debugging cost, high safety risk, and difficult quality evaluation in the current automatic forming and cutting control of roadways, a digital twin driven virtual debugging method for roadway automatic forming and cutting is proposed. Firstly, by using real-time appearance mapping (RTAP-MAP) technology to reconstruct the three-dimensional environment of the roadway, a control system model of the roadheader is constructed to form a virtual debugging environment. The virtual sensor technology is used to achieve precise mapping from physical space to virtual environment state. A performance evaluation method for roadway automatic forming cutting has been established to address the problem of difficulty in quantitatively evaluating the quality of section forming. Based on the recording of the section forming cutting control process in the data transmission center, the evaluation indicators of section forming precision, cutting efficiency and the number of oil cylinder switches, hard rock cutting adjustment, and over excavation and under excavation are mainly calculated. This provides precise feedback signals for the iterative optimization of deep learning algorithms. An automatic cutting control strategy that integrates reinforcement learning is proposed to improve the adaptability and precision of automated operations. To verify the effectiveness and accuracy of the virtual debugging method, an automatic control experimental platform for roadheader is built. The virtual debugging system is applied to the automatic control program for forming cutting of excavation roadway. The virtual simulation results show the following points. ① The maximum positioning errors of the X, Y, and Z axes of the debugged software at the control key points are 74.8, 72.93, 123.67 mm, respectively. It indicates that the positioning precision of the virtual debugging method meets the performance requirements. ② The trajectory of the virtual prototype is basically consistent with that of the physical prototype, indicating that this debugging method has achieved mapping to the physical space. The application result shows the following points. ① The reinforcement learning controller adapts to complex environments in virtual excavation testing, effectively converts virtual sensor inputs into precise control instructions, and verifies the feasibility of simulation reality transfer training. By processing real-time feedback on excavation precision and avoiding over excavation and under excavation, the controller learns and optimizes the strategy. ② The improved section forming cutting control performance has been improved. According to the control quantity timestamp records in the database, it takes 126 seconds, which is 8 seconds less than before the improvement. ③ After improvement, the maximum error in tracking the end trajectory of the cutting section is 6.0 mm, which is 0.3 mm lower than before. This avoids the under excavation caused by the shaking of the cutting trajectory and makes the trajectory and section smoother.
- roadway automatic forming cutting /
- digital twin /
- virtual debugging /
- virtual sensors /
- virtual real coordinate transformation

HTML全文

图 1 巷道自动成形截割虚拟调试系统总体方案

Figure 1. Overall scheme of virtual debugging system for roadway automatic forming cutting

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图 2 虚拟巷道模型

Figure 2. Virtual roadway model

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图 3 掘进机机身及截割部坐标系

Figure 3. Coordinate system of roadheader body and cutting unit

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图 4 虚拟相机定位

Figure 4. Virtual camera positioning

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图 5 二次强化学习训练方法

Figure 5. Secondary reinforcement learning training method

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图 6 截割性能优化二次强化学习结果

Figure 6. Cutting performance optimization and secondary reinforcement learning results

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图 7 虚实同动效果

Figure 7. Effect of moving together with the virtual reality

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图 8 数据存储与俯仰角虚实数据对比

Figure 8. Comparison of data storage and pitch angle virtual and real data

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图 9 巷道断面成形监测人机界面

Figure 9. Human machine interface for roadway section forming monitoring

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图 10 单次断面成形PPO模型优化

Figure 10. Optimization of proximal policy optimization model for single section forming

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图 11 截割性能优化效果

Figure 11. Optimization effect of cutting performance

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图 12 优化后截割头位置误差

Figure 12. Position error of cutting head after optimization

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表 1 截割部连杆参数

Table 1. Connecting rod parameters of cutting unit

连杆	${d_i}$/mm	${a_{i - 1}}$/mm	${\alpha _{i - 1}}$/（°）	${\theta _i}$/（°）
01	0	0	0	θ₁（0±45）
12	0	c₁	−90	θ₂（−90±45）
23	c₂	55	−90	0
34	c₃	0	0	0

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表 2 基于虚拟传感器的机身定位结果

Table 2. Body positioning results based on virtual sensor （mm，mm，mm）

序号	虚拟传感器定位坐标	虚拟空间坐标	误差
1	（840.41, 2132.92, 5806.32）	（809.08, 2060, 5930）	（31.35, 72.93, −123.67）
2	（981.51, 2032.57, 6125.83）	（1058.33, 2060, 6112）	（−76.76, −27.31, 13.78）
3	（926.91, 2005.23, 6432.2）	（933.58, 2060, 6423）	（−6.68, −54.73, 9.17）
4	（1723.83, 1629.84, 7449.22）	（1798.61, 1660, 7498.1）	（−74.8, −30.18, −48.84）
5	（1908.67, 1471.14, 7980.21）	（1915.45, 1510, 7930）	（−6.75, −38.87, 50.2）

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