煤矿井下机车无人驾驶系统的研发和运行,有助于降低因调度、操作失误而发生运输事故的概率并且能够从根本上减少井下作业人员的数量。 

与技术日趋成熟的地面汽车无人驾驶、铁路无人驾驶不同,在井下环境实施机车无人驾驶存在诸多新的挑战:例如运输大巷人车共用、巷道狭窄、光照条件不均匀,无法利用卫星定位系统,缺乏有效的通信手段等。 从实现井下无人驾驶机车系统的关键问题分析入手,综述了其技术研究进展。

 ① 提出了基于信息网络与控制网络无缝结合的井下无人驾驶机车系统架构,以确保列车调度智能化、机车操控与状态采集自动化、运输监控中心与调度中心一体化,并且兼容远程遥控、自主运行等无人驾驶模式;

② 给出智能调度的概念,即无人驾驶系统应当在运输调度智能化的基础上实现,有效促进两个系统间的资源共享、功能协同;

③ 详细比较分析了井下封闭环境定位技术,指出超宽带(UWB)能够有效应对亚米级的机车高精度无线定位且鲁棒性和稳定性方面性能优良,能够满足井下机车无人驾驶对定位精度的要求;

④阐述了适用于井下无人驾驶的数据通信网络覆盖,重点根据实际的工程经验给出接入 WLAN 网络需要满足的性能指标,分析了 5G 新型通信网络即将带来的井下无人驾驶应用突破;

⑤ 在机器视觉用于路况分析方面,探讨了基于轨道模型和基于图像特征的轨道线检测算法、基于深度学习神经网络的目标检测算法、基于双目测量和单目测量的目标距离估计算法、深度学习网络的轻量化技术。最后,展望该领域的技术发展与应用前景。

The research,development and application of the driverless system of the underground locomotive in coal mine is useful to reduce the transportation accidents’ probability caused by scheduling and operation errors and reduce the number of miners used. Different from the mature technology of self-driving cars and trains,there are many new challenges in implementing driverless locomotive in underground environment,such as the sharing of people and vehi- cles in the transportation roadway,narrow roadway,uneven lighting conditions,inability to use the satellite positioning system,the lack of effective means of communication and so on. Starting from the analysis of the key problems in the realization of the underground driverless locomotive system,this paper summarized some technical research progresses. Firstly,the system architecture of underground driverless locomotive based on the seamless combination of information network and control network is proposed to ensure the intellectualization of train dispatching,the automation of locomo- tive control and state collection,the integration of transportation monitoring center and dispatching center,and the com- patibility of remote control,autonomous operation and other driverless modes. Secondly,the concept of intelligent dis- patching is brought about,that is to say,the driverless system should be realized on the basis of intelligent transporta- tion scheduling,which can effectively promote the resource sharing and function coordination between the two systems. Thirdly,the positioning technology of closed underground environment is discussed,and a clear conclusion is drawn that the UWB ( ultra-wide band) positioning can effectively cope with the high-precision wireless positioning of sub meter level locomotives with excellent performance in robustness and stability,which can meet the positioning accuracy of underground locomotive unmanned driving. Fourthly,the data communication network coverage suitable for under- ground is described,the performance indicators required to access WLAN network according to actual engineering ex- perience are listed,and the breakthrough of underground driverless application that the 5G new communication net- work will bring about is also forecasted. Fifthly,with the aspect of using machine vision for road condition analysis,the track detection algorithm based on track model and image feature,the target detection algorithm based on deep learn- ing neural network,the target distance estimation algorithm based on binocular measurement and monocular measure- ment,and the lightweight technology of deep learning network are also discussed respectively. Finally,the development and application prospect of this field are prospected.