为响应智能化煤矿发展需要，同时为改善综掘工作面智能化无人化发展较综采工作面滞后现状，提出了掘进装备自主定型定向截割、掘进装备自主巡航、掘进机健康管理技术及多机多工序智能协同控制技术四大截割钻进装备先进技术并给出研究方向。针对掘进装备自主定形定向截割技术，通过研究不同掘进装备的截割轨迹规划、机身振动特征对截割头轨迹误差的影响规律及不同掘进速度下掘进巷道顶板稳定性分析方法进行掘进装备截割路径及截割速度规划研究;通过研究位姿监测技术、恒功率截割技术、记忆截割技术实现掘进装备自主定形定向截割控制;通过数字采集系统构建掘进巷道数字孪生虚拟模型，采用掘进机虚拟操纵平台实现掘进机远程自动截割控制。通过掘进巷道环境感知技术，构建掘进巷道的三维模型并标明巷道的安全情况;利用激光扫描及多种算法，形成巷道三维点云模型;对激光、通信、视觉及惯性等传感器等进行技术互补性融合，利用多种融合算法，实现高精度巷道感知;采用随钻测量系统进行瓦斯监测和超前探水作业;将视觉感知技术、激光测距技术、全站仪、超宽带定位技术等融入井下巷道的设备定位系统中，实时监测巷道内的掘进机位姿。通过结合掘进机健康管理技术发展现状，梳理出掘进机健康管理技术在故障预警技术、故障诊断技术、寿命评估技术3个方面存在的不足，并针对所面临的挑战提出了跨时空域度的监测信息多源数据融合技术、掘进机与断面岩层耦合作用机理、突发事件风险识别模型、自学习健康管理系统4项拟解决的科学问题。通过多传感器信息融合技术，研究掘进系统截割单元及临时支护单元的控制方法，分析多源信息分析处理及优化策略，以自动截割系统和超前临时支架移架系统为例，阐明了多传感器信息融合的运行机制，提出多工序协同控制技术难点及实现这一目标需要解决的2个关键点。

In order to respond to the development of intelligent coal mine,and to improve the status quo of intel-lectualized and unmanned development of fully mechanized heading face lagging behind the comprehensive mining face,four advanced technologies of cutting and drilling equipment,namely,self-determination directional cutting,autonomous cruise of driving equipment,health management technology of roadheader and intelligent cooperative control technology of multi-machine and multi-process,are proposed and the research direction is given.Aiming at the technology of autonomous and qualitative directional cutting of drivage equipment,this paper studies the influence law of cutting track planning of different drivage equipment,the effect of vibration characteristics of fuselage on the track error of cutting head and the analysis method of roof stability of roadway under different drivage speeds.The control of self-determination and directional cutting of drivage equipment is realized by studying the technology of position and posture monitoring,constant power cutting and memory cutting.The digital twin virtual model of roadheader is constructed by data mining system,and the remote automatic cutting control of the roadheader is realized by using the virtual control platform of the roadheader.Through the envi-ronmental awareness technology of the roadway,the three-dimensional model of the roadway is constructed and the safety of the roadway is indicated.The 3D point cloud model of roadway is formed by laser scanning and many algorithms.The technologies of laser,communication,vision and inertia sensors are combined with each other,and the high-precision roadway perception can be realized by using a variety of fusion algorithms.The gas monitoring and water exploration are carried out by using the on-going measurement system.The technologies of visual perception,laser ranging,total station and ultra wideband positioning are integrated into the equipment positioning system of underground roadway,and the position and posture of roadheader in the roadway are monitored in real time.Based on the development of the health management technology of the roadheader,the shortcomings of the technology of the health management of the roadheader in three aspects of the technology of fault warning,fault diagnosis and life evaluation are summarized.In view of the challenges,the multi-source data fusion technology of monitoring information across time and space,the coupling mechanism between roadheader and cross-section rock stratum,emergency risk identification model and self-learning health management system are proposed as four scientific problems to be solved.Through the multi-sensor information fusion technology,the control methods of cutting unit and temporary support unit of heading system are studied.The analysis and processing of multi-source information and optimization strategy are analyzed.Taking the automatic cutting system and the advanced temporary support moving frame system as an example,the operation mechanism of multi-sensor information fusion is clarified.The technical difficulties of multi-process collaborative control and two key points to be solved for achieving this goal are presented.

1 掘进装备自主定形定向截割技术

   1.1 掘进装备截割路径及截割速度规划

   1.2 自主定形定向截割控制技术

   1.3 掘进机远程自主截割控制技术

2 掘进装备自主巡航技术

   2.1 掘进巷道环境感知技术

   2.2 掘进装备自主移动导航技术

3 掘进机健康管理技术

   3.1 故障预警技术

   3.2 故障诊断技术

   3.3 剩余寿命评估技术

   3.4 拟解决的科学问题

4 多机多工序智能协同控制技术

   4.1 设备自感知技术

   4.2 系统各单元控制方法

   4.3 多工序协同控制技术难点

5 结论