• CSCD核心中文核心科技核心
  • RCCSE(A+)公路运输高质量期刊T1
  • Ei CompendexScopusWJCI
  • EBSCOPж(AJ)JST
二维码

隧道建设(中英文) ›› 2026, Vol. 46 ›› Issue (2): 406-418.DOI: 10.3973/j.issn.2096-4498.2026.02.015

• 施工机械 • 上一篇    下一篇

TBM自动巡航系统研究与应用

侯昆洲1, 任赛楠1, 杨重良1, 龙斌1,2, 齐梦学3   

  1. (1. 中国铁建重工集团股份有限公司, 湖南 长沙 410100; 2. 中南大学机电工程学院, 湖南 长沙 410083; 3. 中铁十八局集团隧道工程有限公司, 重庆 400700)
  • 出版日期:2026-02-20 发布日期:2026-02-20
  • 作者简介:侯昆洲(1985—),男,河南泌阳人,2010年毕业于河南科技大学,机械电子工程专业,硕士,正高级工程师,现主要从事全断面硬岩隧道掘进机电气系统设计研发工作。 E-mail: houkunzhou@crchi.com。

Research and Application of TBM Automatic Cruise Control System

HOU Kunzhou1, REN Sainan1, YANG Chongliang1, LONG Bin1, 2, QI Mengxue3#br#

#br#
  

  1. (1. China Railway Construction Heavy Industry Co., Ltd., Changsha 410100, Hunan, China; 2. College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, Hunan, China; 3. China Railway 18th Bureau Group Tunnel Engineering Co., Ltd., Chongqing 400700, China)
  • Online:2026-02-20 Published:2026-02-20

摘要: 为进一步提升隧道施工的安全、质量和效率,解决全断面硬岩隧道掘进机(TBM)在施工过程中对人工依赖度高、自动化与智能化水平不足的问题,基于大数据与人工智能技术,研发一套TBM自动巡航系统。该系统通过多源传感器融合技术实时感知围岩状态,并据此自主调节刀盘转速、推进速度等掘进参数;同时,结合高精度导向系统提供的TBM实时位姿数据,基于深度迁移学习神经网络实现对设计轴线的实时追踪与机身姿态动态调整,从而完成自动纠偏与调向;此外,系统还实现了换步流程的全自动协同控制。该系统已应用于北山地下实验室“北山1号”TBM工程。应用结果表明: 1)在无人干预条件下,TBM能够自主识别围岩状态并动态匹配掘进参数,成功实现了纵坡10%条件下的自动连续下坡掘进与255 m半径水平转弯。2)系统在掘进、纠偏与换步等核心流程中实现了高度自主运行,最终全线轴线偏差控制为±50 mm,单循环换步时间不超过5 min。该系统的研发与应用,证实了TBM在复杂工序下实现长期自主巡航的可行性,为智能化掘进技术的工程化推进提供了实践范例。


关键词: 自动巡航, 无人驾驶, 一键启动, 围岩识别, 参数自适应, 自动纠偏调向, 自动换步

Abstract: At present, tunnel boring machine (TBM) tunneling depends heavily on manual operation, resulting in limited automation and intelligence. To further improve tunneling safety, quality, and efficiency, an automatic cruise control system for TBM tunneling based on big data and artificial intelligence technologies are designed and developed. The system employs multisource sensor fusion to perceive surrounding rock conditions in real time, enabling autonomous adjustment of tunneling parameters such as cutterhead rotation speed and thrust velocity. Meanwhile, by integrating real-time TBM position and attitude data provided by a high-precision guidance system, a deep transfer learning neural network is used to achieve real-time tracking of the designed alignment and dynamic adjustment of the TBM attitude, thereby enabling automatic deviation correction and directional control. In addition, the system realizes fully automated collaborative control of the stepping process. The system has been successfully applied in the Beishan No. 1 TBM project of the Beishan underground laboratory. Application results demonstrate that (1) under unmanned conditions, the TBM autonomously identifies surrounding rock conditions and dynamically matches tunneling parameters, successfully achieving continuous downhill tunneling under a 10% longitudinal slope and completing a horizontal turn with a radius of 255 m; and (2) the system achieves highly autonomous operation throughout core processes, including excavation, deviation correction, and stepping, ultimately controlling the deviation of the entire tunnel axis within ±50 mm and completing each single-cycle stepping process within no more than 5 min. The successful development and application of this system confirm the feasibility of long-term autonomous cruising of TBMs under complex construction conditions and provide a practical engineering example for advancing intelligent tunneling technology.

Key words: automatic cruise, unmanned driving, one-click start, surrounding rock indentification, parameter adaptive, automatic deviation correction and steering adjustment, automatic stepping