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水泵平衡装置设计

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目录.doc

前言………………………………………………………………………………1

在矿山中水泵的应用…………………………………………………………3

矿山对排水设备的要求………………………………………………………3

常用主排水泵结构……………………………………………………………3

平衡盘工作原理………………………………………………………………4

产生轴向推力的主要原因……………………………………………………4

液体静压支承原理……………………………………………………………8

流体静压技术的简述…………………………………………………………8

油腔的流量及有效面积的计算推导…………………………………………8

基本矩形油腔的流量计算…………………………………………………8

环行油腔推力轴承的流量计算原理………………………………………9

节流器的流量计算…………………………………………………………12

小孔节流器………………………………………………………………12

节流比和液阻比………………………………………………………13

液体静压支承的承载能力计算……………………………………………13

单油腔静压支承…………………………………………………………13

对称等面积对置油腔静压支承…………………………………………14

液体静压支承的油膜刚度计算……………………………………………15

油膜刚度的概念…………………………………………………………15

单油腔静压支承油膜刚度计算…………………………………………16

总体设计方案…………………………………………………………………18

水泵的轴向推力计算………………………………………………………20

平衡装置结构设计计算……………………………………………………20

止推板尺寸计算…………………………………………………………20

油泵供油压力计算………………………………………………………20

节流器选择………………………………………………………………21

油垫中油膜刚度及最大位移的计算……………………………………22

油膜的流量及功率………………………………………………………23

液压控制系统设计……………………………………………………………25

选择液压泵…………………………………………………………………25

选择电机……………………………………………………………………26

阀类元件及辅助元件………………………………………………………27

液压系统的性能验算………………………………………………………36

系统压力损失的验算……………………………………………………36

油液发热温生计算………………………………………………………38

设计及使用时的注意事项…………………………………………………39

经济性评估……………………………………………………………………40

结论……………………………………………………………………………41

致谢………………………………………………………………………………42

参考文献…………………………………………………………………………43

摘要

研究的目的是采取液体静压支承来平衡轴向力的方法。改善矿山排水装置平衡盘受力状况，解决离心水泵轴向力引起磨损严重的问题。延长水泵使用寿命，提高效率，降低矿井排水装置的维修费用。液体静压支承是借助于输入支承工作面间的液体静压力来支承载荷的滑动支承。它处于纯液体润滑条件下工作。液体静压支承具有速度范围宽、支承能力大、运动精度高、抗振性能好和使用寿命长等优点。但液体静压支承需要一套液压供油系统，润滑油的过滤精度要求较高。本文以150D309型泵轴向力的液体静压支承平衡为例。并且适合在各种型号的矿用的水泵的改装，起到代替平衡盘的作用。因为盘之间的较小间隙处容易堵塞，另一方面，固体颗粒会加快平衡盘的磨损，造成平衡盘与平衡环之间的间隙增大，达不到原有的平衡作用。所以矿用水泵可广泛采用液体静压支承来平衡轴向力。

关健词：多级离心泵；液体静压支承；轴向力；平衡盘

1 在矿山中水泵的应用

　　　本章重点在于对离心式水泵的结构以及工作状态做了简要的介绍，并且对离心式水泵轴向推力的推导过程，做了详细的分析。

1.1 矿山对排水设备的要求

　　　井下排水设备要求水泵有一定的排水能力，应该在20小时内排出24小时的正常涌水量。工作水管应该配合水泵，在20小时内排出24小时的正常最大涌水量。工作水泵机组必须工作可靠。

　　　水泵在工作时会产生很大的轴向力。水泵因为轴向力引起的磨损非常严重，特别是多级水泵。多年以来一直采用的是平衡盘平衡的方法，但是没有从根本上解决这个问题。所以为了改变旧的平衡方法，把液体静压力支承的理论应用于多级离心泵的平衡装置上。有很好的前景。

1.2 常用主排水泵结构

　　　多级分段式水泵的结构是多样的，目前使用最为广泛的D型水泵有一定的代表性，如图1-1所示。像其他形式的水泵一样，其水力部件包括叶轮、导水圈、反水圈、出水段和进水段。2 液体静压支承原理

　　　本章重点在于对静压支承的静态特性的结论的推导过程，做了详细的分析。进一步说明了应用静压支承来平衡多级泵轴向力是非常有效的方法。

2.1 流体静压技术的简述

　　　液体静压支承是借助于输入工作面间的液体压力来支承载荷的滑动承载。它处于纯液体润滑条件下工作。

　　　液体静压支承可按使用要求分为向心、向心推力等三种类型

　　　液体静压支特点

速度范围宽。

承载能力大。

运动精度高。

抗振性能好。

使用寿命长。

但是液体静压支需要一套液压油系统，润滑油的过滤精度要求较高。

2.2 油腔的流量及有效面积的计算推导

2.2.1 基本矩形油腔的流量计算

　　　润滑油沿x方向流过两平板之间隙的情形，间隙为h，间隙长度为a，两端油压分别是P及0由液体润滑理论基础得

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内容简介：: 中文题目：水泵平衡装置设计外文题目： Water pump balancing Installment毕业设计（论文）共 64 页（其中：外文文献及译文20页）图纸共3张完成日期 2006年6月答辩日期 20年月辽宁工程技术大学毕业设计（论文）附录A1999年 8月国际会议的记录关于机器人服务领域对于安置测量的自动化采矿机械Anthony Stentz, Mark Ollis, Steve Scheding, Herman Herman, Chris Fromme, Jorgen Pedersen, Tim Hegadorn, Robert McCall, John Bares, and Rich Moore. 机器人学学院卡内基梅隆大学匹兹堡PA 15213摘要地下采煤业是产业很适合于机器人自动化。人工在黑暗多灰尘，还有许多障碍物的环境下进行采矿工作。对生产机器加以轻度改进可能减少每个机器每天另外的收支，共计数以万计美元。自动化采矿机械迄今依靠基础设施引导设备。实际生产发现这种方法不合适采矿工作的进行，所以它未被采取。我们的方法使用机械总线摄象机作为引导设备。这种方法可以运用天然基础设施和常用设备较好的煤矿中。我们已经表明,我们的做法符合采矿机器直接采煤的要求,并且可以维持每开采的正常循环。在最近的几个月里，采矿技术迅速地接近白特形式。1. 前言例如煤炭这样的软型材料的采矿产业是大的产业。全世界有总共435百万吨煤炭每年被生产。许多煤炭的位置处在地表下深处，若要开采必须除去很深的地表。二个主要过程为开采煤炭地是长壁开采法采矿和室和柱子采矿。使用长壁开采法采矿可以迅速地切开煤炭并且在一台传动机为从矿的撤除轨行剪床的系统中放置它。当他们沿路轨上，可以使移动水力顶板支护保护剪床。在完全通行面孔，剪床，路轨和传动机后，并且推进顶板支护和屋顶崩溃支护后边。室和柱子采矿方法应该使用机器合奏削减格子网络，如(图1)。室和柱子采矿方法不同于在长壁开采法，这不象在长壁开采法采矿中取消仅某些煤炭被移动; 方形的柱子将被用来支持屋顶。煤炭是由一台连续采煤机开采，被机器的刀头切割后由一个轨道从一个表面运到另一个表面，如(图2)。机器工作时刀头上升到采煤工作面，然后切除那些比较大的煤炭块。煤炭被会集运载机器的尾巴的一台在机上传动机。尾部放置在一条电机传送带上。煤炭被运输到矿的外面，使用一台更加耐久性强的传动机。在把煤炭的部分切成与机器的要求大致相等的长度以后，连续采煤机撤退，并且顶板锚杆支护机器安装螺栓压缩支撑屋顶，减少屋顶倒塌的机会。要十分注意，很重要的一点是连续的采矿机对地下采矿的两个类型是关键的。在用长壁开采法采矿时，部分机器使用开发安置传动机的装置，提供透气，并且使长壁开采法的设备是可以被替换的。典型采煤机连续运动是慢速的运动(典型地大约30米前进每12小时一换班)，并且他们在长壁开采法的采矿构成瓶颈。2. 采矿自动化的目的煤矿是黑暗的、肮脏、潮湿，而且空间比较小。被切割的碳块不能大于采矿机。经常矿洞是很低，所以操作员不可能站立挺直。在矿工切开煤炭时，空气充满了大量的尘土。为了安全的方面考虑，典型的操作员应该先在机器后面操作，而后按钮时在跑到箱子后面。由此可见工作环境是非常差的，而且操作员容易疲劳，生产效率减慢，并且容易犯错误。在地下矿井中自动化采矿机到被采矿用数量的计算方式：生产力的增加；当操作员疲劳的时候，他们容易在操作中出现错误，并且动作也比较慢。这两向都会导致失去的生产力。为一个整个转移工作，自动化可能操作设备，使其效率提高。在每个每天的机器生产力提高1%，可能意味着数以万计美元另外的收支。降低业务成本：维护是总采矿费用的一个重要的部分。机器要求定期的维护，包括完全改建根据固定的日程表，否则容易导致失去的生产。自动化可能保证机器工作在一个封闭的环境因而使减到最小每天的磨损和加长正常运行时间。劳动力是另一重大费用。自动化可能通过使每名工作者监督几个机器减少人工成本。错误量少；采矿要求一些精确度。如果采矿机器极大偏离，需要另外的固定。在例外情况，煤块必须是再切开了。在特殊的情况下，煤块必须是再切开了。这两个结果都有一些损失。自动化可能增加精确度到采矿行动。安全的提高；安全性提高的关键是找出塌方、爆炸，并且机器故障发生可能的可能性。自动化消灭采矿机器之间紧密运动关系，从而使操作员监测机器从更长，并且安全距离也更长。3. 自动化战略自动化是采取慢采矿的方法来采地下采矿。早先方法，主要在坚硬岩石采矿，要基台，较轻的管道，或引导设备的其他基础设施13。这另外的基础设施是不被需要的，因为它可以损坏机器或破坏环境。此外，随着采矿时间的延长，新的基础设施是必要。我们的方法将利用矿的自然结构，当前广泛使用的矿山机械设备。裂缝是有确切的厚度，然而，是一般未知的，使得操作控制更加困难。有二种勘察在煤炭之间岩石的裂缝的方法6：1) 前摄：表面渗透的传感器例如雷达和伽玛探测器; 2) 反应：红外照相机可以查出岩石被触击的情况。空气的检测控制是在这工作的范围之外。机器的剩余的状态参量是它的位置(3)和取向(3)。总共是6点，其中最重要的3个方面为，沿着一条参考的轨道运动。我们提到测量和控制第一二当标题，第三标题是废油坑深度控制。开始的控制保证煤块适当地被度量的被削减，可以让长壁开采法的设备和输送机系统合并在一起。从测量员的参考标号中得，必需的规格是+ - 10 cm。深度控制保证截煤机的截槽数量的多少以及煤炭每个废油坑检测周期。要使电机的效率与煤炭传动机匹配。必需按规格要求移动2%距离。本文会对控制和废油坑深度控制详细叙述。最初，我们计划发布这些技术作为操作员的帮助，提供设置中错误的警告给操作员。此后，我们将发布技术作为半自动的控制系统，人的监督下由计算机关闭控制回路。最后，我们开发有能力测量更多机器的参量，我们将自动化机器周期的更大的部分，最大化机器和工作者生产力。最终，我们开发更有多测量能力机器，我们将自动化机器使用周期延长，可以充分发挥机器的效率和工人的生产力。4. 开始控制开始控制系统的目的是测量采矿机的侧面垂距。要求采矿机停留10厘米大小的一个被预先决定的道路的侧面，垂距之内为距离大于100米。如果完成此要求，机器标题和侧面垂距的控制(标题和侧面垂距控制的定义在Figure3显示)。我们使用了一定数量的传感器类型作为监控机器。在不用对所对应工作环境外的基础设施时，我们首先试图设计系统维护功能。一个磁性指南针将帮助机器在正确的方向工作。然而，如果磁场的变化是足够大，在地球的表面，增加10个或更多的错误。继续发展，这个错误可以导致整个系统的毁坏，因此它不可能随着时间发展下去。如果在一条100米路线里有一个错误，就能导致10米左右的距离内有错误。很明显，单独指南针是不足的; 由于周围环境噪声的影响，使它难与其他传感器结合用在一个系统里。虽然他们处于长期工作中，在短时期内陀螺仪是极端精确测量角度。程度噪声在范围从分钟的时标可以增加到几小时，根据传感器的质量。程度噪声的范围可以从分钟增加到几小时。因为它有时要在几天之间完成勘测工作，单独陀螺仪不可能执行这种任务。他们顺利地工作，并且他们被用在地下采矿自动化5 7 然而，他们还是受到随机的噪声影响。我们也考虑到他们有对一个或二个轴进行扫描器的用途的作用。这样设备可能使用，来保证轨道不产生移动。这些设备早先就被测试过了8。并且根据来示特功能，他们可以被准确性修造，回数可以在一百米或更远的更距离反馈。不幸地，这个传感器的可靠性没有被保证。在特定的角度来看，矿场的采煤设备的改变，要由人员，堆煤炭，或在矿上的房顶高度来决定。另外，目前这样设备的效率是不足够对于使用地下在采煤机连续的工作。最后，我们认为一个控制系统使用单独使用在采煤机器上是不切实际的。反而，我们利用了矿测量员的数据，开发一个系统。测量员当前安装这些设备为了引导操作员。为了引导使用设备，我们做了这些同样的解答。另外，我们增加了一台陀螺仪，允许系统工作在十分钟到二十分钟没有接受任何反馈的情况下。工作人员，使用一个柱面透镜传播激光穿入一个垂直平面，作为使用的参考。使用二块平行的钢板附加采矿机所在的工作地点。一台附加机器的照相机，在一个已知工作的地点仅可以同样频率的过滤器光以波长和把图象传递给计算机。目标的图象由图5所显示。在这个例子中，风扇与照相机是属于不同的系统的部分。使用条纹系统和传导系统，计算机在图象中辨认那些映像点(参见图6)。在照相机的参照系中，这些点用于估计计算包含风扇的一个电机。这平面系统被变换成矿工的坐标平面; 然后，照相机可以被使用来估计计算。通过检查一个内部模型的图象，系统能查出许多错误的形式; 例如，封锁，外部光线来源，错误的目标等。视觉系统的产品是与在电机上光导纤维的设备同样的产品。从熔化的数据来看，这两个传感器单独比较，看出传感器有这样不同的物产。视觉系统是一个完整的感器，但超出测量范围(0.5厘米到1厘米垂距)时相当粗糙的设备。经测精度是相对的，但对非常高的辨率的测量是非常准确。由这两个传感器，这种设备比可单独从视觉系统得到，仍然提供好的准确性。这两个传感器的使用允许不使用系统可以不受噪音的视觉系统的影响; 标题控制可以起作用在10-20分钟内，不用任何视觉系统的控制。5深度的控制深度控制系统的目的是测量或控制采矿机短距离的移动。但是，我们对这项任务必须考虑的许多标准方法才能应用。在轨道安装编码器是最明显的解答。然而，这将要求重新设计履带。这是一项极端费钱和费时的任务，并且将必须对每个设备的设计分开地做。进一步发展，轨道在那些将无效编码的连续采煤机时滑掉。因为这些原因，所以我们选择不使用编码器。从过载信号中产品能加大测量距离。然而，工作的速度足够小 (当切开煤块使用几微秒)时就能迅速克服噪声信号。我们考虑使用这个系统来检测参考点。作为控制的这点，在这种环境里这种解答不能接受。由于矿人员和设备的长时间的工作，难以保证的一个固定式参考点不发生变化。最后我们选择几个能对矿顶和肋部测试立体声照相机的系统，如图7所显示。又如图8所显示，, 由于矿表面是足够地光滑，所以它的特点可以容易地被跟踪。运用这些特征。对矿井肋部和矿顶都可以被测量，对于不同于机器，肋部和矿顶倾仍然使用同样的系统。每个立体声模块包括二台照相机装备以及滤波器和一个散开的光源。如图9所显示，我们选择使用红外线发射的装置作为光源。只要安装在圆环台照相机附近，这些光源就能散发外部可见的光谱。作为这些光源散发外部可见的光谱，他们不干涉人工正常的工作。带通滤波器被选择要与精确光源波长的散开匹配。从而要求减少其光源的能量。而后发展的是同时被数字化二台照相机。每个立体系统然后执行以下处理步骤：1)从采煤工作面图象中四个特点窗口被挑选。在多个方面这些特点被这个选择四个窗口系统使用。2) 使用立体声视觉系统，他每个特点窗口的中心的位置都被计算4。为了完成此项目，我们必须利用照相喷气推进实验室提供的标准软件2。3)正常化的交互作用，下一个阶段它的特点从同一台照相机上跟踪到一个图象。而后，每个窗口中心的新的3D位置被计算。4) 在二个框架之间照相机行动被计算，通过使用一种最小平方的算法，在第一套3D点位置和第二个集合之间。我们试验了获得全部六个自由度。但我们解决当前系统为四个自由度：三个旋转和一自转。5) 在图象的边缘附近有移动的特点，选择新的特点作为第1步，否则，继续带着最初的特点。6) 第3步进入每一个立体系统寄发的结果都对应一个中央模块。从一个或更多照相机模块中，中央模块会集出位置。并且只有通过过滤他们可以获得，采矿机工作的最佳估计方法。每一个照相模块都对应煤炭肋部或矿顶的一个不同的部分。像这样，即使一个或更多照相模块发生故障，机器仍然行动可以从剩余的照相模块估计出来。6 结果测试这个系统主要有两点上注意。大多数测试都在我们的仿真的矿上执行。在机器人学学院，完成12CM-12矿工上的实验。另外由在一个附近的无生产地下煤矿中，进行了实验。我们仿真的矿包括词条大致40米长和7米宽的矿井。整个区域被黑塑料包围，目的是遮蔽外部光线。从多个实际矿采取的被熔铸的聚氨酯模子来模仿的矿肋的部分。这些模子被熔铸并且图上黑色，因此他们在颜色和纹理和对废油坑深度照相一样的煤炭肋骨，与实际中的几乎相似。在一定的范围内，矿屋顶由实际矿屋顶的数字式相片马赛克制作。在这个大模型里面测试废油坑深度控制系统(使用仅一个立体系统)总共有两种方式。第一，它被测试了作为操作员的助手，在这些位置中通过简单的图形显示，测量系统的性能。在这容量，1-2米工作状态与变化在50和200 cm之间的煤肋有些相同。与实际地下的情况相比有2%到95%的差距。在Joy采矿工程师的支持下，我们把这个传感器到连接在机上机器控制器上。它的工作状态充分地显示了他的自动化。他的自动化行动的准确性被测量在2%之内。标题控制系统被测试和在一个闭环的控制系统，作为矿工的助手。在2厘米之内这个系统被测量0.3度。作为一个闭环系统，根据这种路线矿工进行编程。我机器转向控制是有些无法预测的，主要由于采煤工作面的典型设备提供的缺乏。但是，系统能跟踪这条路线以在5厘米以下的错误。两个系统也被测试了(在他们的矿工助手)，废油坑深度系统中的10个电机也被测试了。其中导致错误在以下2%。当我们无法测量地面实况的表准时，在距离80米的地方，从我们的被模仿的矿中，测量的变化的结果。测试是在重水和尘土这两个系统的情况执行的。控制系统未受由这些主要问题的影响。由于它单一的功能会持续很久，当大量的尘土可能破坏废油坑深度控制系统时，在实际的生产中，灰尘不是问题。另外测试在几个工作的矿，包括近一段时间对多个废油坑深度和照相系统，这两个系统做出的计划。最后，一名专业连续采煤机操作员被带来，并且有评估两个系统当操作员援助。在我们的矿大模型操作机器以后半天的时间里，他表达对这两个系统增加生产性能的看法。7 结论地下采矿依然是优先考虑自动化的地方，因为自动化可以增加生产效率，节省成本。我们的战略使自动化对自动化采矿任务得到优化，介绍他们并且让矿工得到援助，并且逐渐增加自动化的水平和加宽产业路线。最后，我们开发两个开采的援助的方法。一个系统是控制一台连续采煤机的废油坑深度，另一个系统是控制机器的前端。两个系统利用自身的优势已经出现在基础设施和设备中。在一个实际的高尘土和喷水的情况下的地下矿中他们成功地被使用。在这些方面是成功的，在以后18个月技术的测试将商业化的被推广。致谢作者感谢Bryan Campbell, Dave Herdle, and Frank Higgins for their collaboration on this project. Support was provided by NASA under grant number NCC5-223 and Joy Technologies, 等参考文献1 Baiden, G. “Multiple LHD Teleoperation and Guidance at Inco Limited”, Proceedings of the International Mining Conference, 1993.2 Gennery, Donald.“Least-Squares Camera Calibration Including Lens Distortion and Automatic Editing of Calibration Points”. To appear in Calibration and Orientation of Cameras in Computer Vision, A. Grun and T. Huang, Springer-Verlag.3 Herteau, R. et. al. “Optical Guidance System for Underground Mine Vehicles”, Proceedings of 1992 ICRA.4 Horn, Berthold. Robot Vision. McGraw Hill, 1986, pp.299-333.5 Makela, H. et al. “Navigation System for LHD machines”. Intelligent Autonomous Vehicles, 1995.6 Mowry, G.L. “Promising Coal Interface Detection Methods”. Mining Engineering, January, 1991, pp. 134-138.7 Scheding, S. et al. “Experiments in Autonomous Underground Guidance”, Proceedings of 1997 IEEE Conference on Robotics and Automation, April, 1997, pp.1898-1903.8 Shaffer, Gary, and Stentz, Anthony. “A Robotic System for Underground Coal Mining”. Proceedings of 1992 IEEE Coneference on Robotics and Automation, May,1992, pp. 633-638.附录 In Proceedings of the International Conference on Field and Service Robotics, August 1999Position Measurement for Automated Mining Machinery Anthony Stentz, Mark Ollis, Steve Scheding, Herman Herman, Chris Fromme, Jorgen Pedersen, Tim Hegadorn, Robert McCall, John Bares, and Rich Moore. Robotics Institute Carnegie Mellon University Pittsburgh PA 15213 Abstract Underground coal mining is an industry well suited for robotic automation. Human operators are severely hampered in dark, dusty, and cramped mines, and productivity suffers. Even a slight improvement in productivity can amount to thousands of dollars of additional revenue per machine per day. Automation to date has relied on infrastructure to guide the equipment. The industry finds this approach unsuitable, and it has not taken root.Our approach uses machine-mounted video cameras to guide the equipment. It utilizes natural infrastructure and equipment commonly used in mines. We have demonstrated that our approach meets the requirements for cutting straight entries and mining the proper amount of coal per cycle. The technology is rapidly approaching beta form and will be deployed in several mines in the coming months.1. Introduction The mining of soft materials, such as coal, is a large industry. Worldwide, a total of 435 million tons of coal are produced per year. Much of the coal is deposited in seams underground, located too deep to remove from the surface.The two primary processes for mining coal underground are longwall mining and room and pillar mining. Longwall mining employs a system of rail-mounted shearers that rapidly cuts the coal and deposits it on a conveyer for removal from the mine. Hydraulic roof supports protect the shearers as they move along the rail. After a complete pass along the face, the shearer, rails, conveyer, and roof supports are advanced, and the roof collapses behind. Room and pillar mining uses an ensemble of machines to cut a lattice network (Figure 1). Unlike in longwall mining, only some of the coal is removed; the square pillars are left behind to support the roof. The coal is mined by a continuous miner, a track-driven machine with a rotating cutter head for shearing coal from the face (Figure 2). The machine drives into the coal face with cut-ter head raised, then shears down to the floor to remove a block of coal. The coal is gathered onto an on-board conveyer which carries it to the tail of the machine. The tail deposits it either in a shuttle car or on a mobile conveyer belt. The coal is transported to a more permanent conveyer and out of the mine. After cutting a section of coal roughly equal to the length of the machine, the continuous miner retreats and a roof bolting machine installs bolts to compress the roof strata and reduce the chance of roof fall. It is important to note that the continuous mining machine is crucial to both types of underground mining. In longwall mining, the machine is used to develop the entries in the section that house the conveyers, provide ventilation, and enable the longwall equipment to be emplaced. The typical speed for continuous miners is slow (typically on the order of 30 meters of advance per 12 hour shift), and they constitute the bottleneck in longwall mining.2. Motivation for Mining Automation Coal mines are dark, dirty, wet, and cramped. The entries are not much larger than the machines that cut them. Often the seams are so low that the operator cannot stand upright. As the miner cuts coal, dust fills the air. To be safe, the operator typically stands behind the machine and runs it via a button box. Visibility is poor, the operator fatigues, production slows, and mistakes are made. Automation promises to add value to underground mining in a number of ways:Increased productivity: operators take breaks during the course of a shift and slow down as they fatigue. Both lead to lost productivity. Automation can operate the equipment a peak rates for an entire shift. Even a 1% improvement in productivity can mean thousands of dollars of additional revenue per machine per day. Lower operational costs: maintenance is a significant portion of total mining cost. The machines require regular maintenance, including a complete re-build according to a fixed schedule, leading to lost production. Automation can ensure that a machine is operated within its performance envelope, thus minimizing everyday wear and tear and lengthening uptime. Labor is another significant cost. Automation can reduce labor costs by enabling each worker to oversee several machines.Fewer errors: mining requires some precision. If the heading of an entry deviates significantly, additional roof bolts are required. In the extreme case, the entry must be re-cut. Both result in lost revenues. Automation can add precision to the mining operation.Greater safety: the key to safety is to locate the operator away from the face, where roof fall, explosions, and machine accidents are most likely to occur. Automation eliminates the need to observe the machines actions up close, thereby enabling the operator to monitor the machine from a longer, and safer distance.3. Automation StrategyAutomation has been slow to take root in underground mining. Previous approaches, primarily in hard rock mining, require beacons, light tubes, or other infrastructure to guide the equipment 13. This additional infrastructure is undesirable, since it can be damaged by the machines or dislodged as the ribs and roof shift. Furthermore, new infrastructure is needed as the mine is extended. Our approach is to capitalize on the natural structure of the mine itself, coupled with equipment widely used in mines at present.The exact thickness of the seam, however, is generally unknown, making boom control difficult. Two approaches have been pursued for detecting the boundary between coal/rock in situ 6: 1) proactive: surface-penetrating sensors such as radar and gamma detectors; 2) reactive: infrared cameras to detect heat generated as rock is struck.Boom control is outside the scope of this work. The remaining state parameters of the machine are its position (3) and orientation (3). Of these six, the three most important are heading, lateral offset from a heading reference line, and distance traveled along this reference line. We refer to measurement and control of the first two as heading control and the third as sump depth control. Heading control ensures that entries are cut straightand properly dimensioned to accommodate longwall equipment and conveyer systems. The required specification is +/- 10 cm lateral error from a surveyors reference markings. Sump depth control ensures that the machine cuts the proper amount of coal per sump/shear cycle. This is important for matching the capacity of shuttle cars that haul the coal to the conveyer. The required specification is+/- 2% of distance traveled. This paper discusses heading control and sump depth control in depth. Initially, we plan to release these technologies as operator aids, providing the operator with a measure of error from a desired setting. Later, we will release the technologies as semi-autonomous control systems, closing the control loop by computer with human supervision. Finally, as we develop the capability to measure more of the machines parameters, we will automate larger portions of the machines cycle, maximizing machine and worker productivity.4. Heading control The purpose of the heading control system is to measure heading and lateral offset of a mining machine. The mining machine is required to stay within 10 centimeters of lateral offset of a predetermined path for distances of up to 100 meters or more. Accomplishing this requires control of both the machines heading and lateral offset (definitions of heading and lateral offset are shown in Figure3). We considered a number of sensor types for monitoring and controlling the machines heading. We first attempted to design a system capable of maintaining heading without the use of any off-board infrastructure. A magnetic compass would help keep the machine oriented in the right direction. However, the local variance of the magnetic field is large enough, even on the earths surface, to add 10 or more degrees of error. Further, this error can be quite systematic, so that it cannot be averaged away over time. Integrating this error over a 100 meter course might potentially result in errors of 10 meters or more. Clearly, a compass alone is insufficient; and the difficulty of modeling its noise characteristics make it difficult to combine with other sensors in a systematic way. Gyroscopes are capable of measuring angle extremely precisely for short periods of time, though they tend to drift over longer timescales. A degree of noise can be added on timescales ranging from minutes to hours, depending on the quality of the sensor. Since it may be several days between surveys, clearly a gyroscope alone cannot perform the task. They do degrade smoothly, however, with noise that is well modeled by a random walk; and they have a history of use in underground mining automation 57. We also considered use of a one- or two- axis laser range scanner. Such a device could be used to “sight” backwards along the mine entry to ensure that the path has not drifted. These devices have been tested in underground navigation previously 8, they can be built with angular accuracies of a fraction of a degree, and depending on the power of the laser, can return data at distances of a hundred meters or more. Unfortunately, the visibility of this sensor is not guaranteed. At any given time, the view back from the miner may be occluded by equipment, personnel, piles of coal, or changes in the mine roof height. Additionally, at present such devices are neither robust enough nor cost effective to be used underground on continuous miners. In the end, we decided that a heading control system using purely on-board components was impractical. Instead, we developed a system which makes use of the mine surveyors laser. These lasers are currently installed by the surveyors in order to guide the operators. Our solution uses these same lasers for guidance. Additionally, we added a gyroscope, allowing the system to function for ten to twenty minutes without receiving any readings from the laser. board laser, using a cylindrical lens to spread the laser beam into a vertical plane, is used as a reference. The laser strikes two parallel steel plates attached to the mining machine at known locations. A camera, attached to the machine at a known location and equipped with a filter to pick up only light with the same wavelength as the laser, images both targets in a single image and transmits the image to a computer equipped with a framegrabber. A schematic of the fan laser, targets, and camera is shown in Figure 5; in this example, there is a heading misalignment between the fan laser and the camera/target system.Using a stripe operator and Hough transform, the computer identifies those pixels in the image which are illuminated by the laser (see Figure 6). These points are used to calculate a least-squares estimate of the plane containing the fan laser in the reference frame of the camera. This plane is transformed into the coordinate plane of the miner; finally, heading and offset of the camera are computed from the plane estimate. By checking the thresholded image against an internal model, the system is able to detect many failure modes; for example, blockage of the laser, extraneous light sources, bent target plates, etc.The output of the vision system is then Kalmanfiltered with the output of an onboard KVH fiber-optic gyro. Fusing data from these two sensors provides a much better estimate than from either alone, since the sensors have such different properties. The vision system is an absolute sensor, but returns measurements at a fairly coarse resolution (0.5 degrees of rotation and 1 centimeter of offset). The gyro measures only relative rotations, but to very high resolution and very accurately over small time scales. By Kalmanfiltering the two sensors, the drift of the gyro can be avoided while still providing much better rotational accuracy than is available from the vision system alone. The use of these two sensors also allows the system to reject bad or noisy data from the vision system; the heading control can function for 10-20 minutes without any readings from the vision system at all.5. Sump depth control The goal of the sump depth control system is to measure or control the forward motion of the mining machine over short distances. Unfortunately, many of the standard methods we considered for this task have serious drawbacks for this application. Installing encoders on the tracks was perhaps the most obvious solution. However, this would have required redesign of the actual track assembly. This is an extremely expensive and time-consuming task, and would have to be done separately for each miner design. Further, the tracks on the continuous miner often slip, which would invalidate the encoder readings. For these reasons, we opted not to use encoders. The output from an accelerometer could be doubly integrated to give a distance measurement. However, the velocities of the miner are small enough (a few centimeters per second while cutting) and irregular enough that the integrated noise would quickly overcome the signal. We considered using a laser rangefinding system looking backwards at a reference point. As was the case with heading control, this solution was deemed unacceptable because of the uncertainty in the environment behind the machine; due to the constant passage of mine personnel and equipment, it would be difficult to ensure that a stationary reference point remained in view. We ended up selecting a system which makes use of several stereo camera pairs aimed at the roof and ribs of the mine, as shown in Figure 7. As can be seen in Figure 8, the mine surfaces are textured enough that features can be easily tracked; and using these features, the relative motion of the miner to the ribs and roof can be measured. Unlike the view behind the machine, the view to the ribs and roof tends to remain unblocked. Each stereo module consists of two cameras equipped with band-pass filters and a diffuse light source, as shown in Figure 9. We opted to use a collection of infrared-emitting LEDs as the light source, mounted in a ring around each camera. As these light sources emit outside of the visible spectrum, they dont interfere with the human operators vision. The band-pass filter was selected to precisely match the wavelengths of the diffuse light source, thereby strongly reducing the effect of other incidental light sources. Incoming video from the two cameras is digitized simultaneously. Each stereo system then performs the following processing steps: 1) Four feature windows are selected from a coal face image. These features are selected using an interest operator which selects four windows which have strong texture in all directions. 2) Using stereo vision, the 3D position of the center of each feature window is calculated 4. In order to accomplish this, we make use of camera calibration software provided by the Jet Propulsion Laboratory 2. 3) Using binarized normalized correlation, the features are tracked to an image from the same camera during the next time step. Again, the new 3D position of each window center is calculated. 4) The camera motion between the two frames is computed by using a least-squares algorithm to fit the correspondence between the first set of 3D point positions and the second set. We have experimented with obtaining all six degrees of freedom of this motion, but our current system only solves for four: three degrees of translation and one rotation. 5) If the features have drifted near the edge of the image, select new features as in step 1; otherwise, continue with the original feature set. 6) Loop to step 3. Each of the stereo systems sends its results to a central module. The central module gathers the position estimates from one or more of the camera modules, and Kalman filters them in order to obtain a single, best estimate of the motion of the mining machine. Each of the camera modules is mounted to view a different part of either the coal rib or the coal roof. In this way, even if one or more of the camera modules fails, machine motion can be estimated from the remaining camera modules.6. Results Testing of this system has been performed primarily in two locations. Most of the testing has been performed in our simulated mine, complete with a Joy 12CM-12 miner, on site at the Robotics Institute. Additional testing was performed in a nearby non-production underground coalmine (the “Tour-Ed” mine.) Our simulated mine consists of an entry roughly 40 meters long and 7 meters wide. The entire area is enclosed in black plastic, to block out extraneous light. Simulated mine ribs were created from cast polyurethane molds taken from multiple actual mines. These molds were then cast in plaster and painted black, so that they appear to the sump depth cameras as similar as possible to actual coal ribs in both color and texture. The mine roof is made from a mosaic of digital photographs of actual mine roof, blown up to the proper scale. A sump depth control system (using only a single stereo pair) was tested inside this mock-up in two ways. First, it was tested as an operator aid, in which position measurements from the system are conveyed back to the miner operator via a simple graphical display. In this capacity, repeated sumping motions of 1-2 meters were performed with the rib varying between 50 and 200 cm from the sideview cameras. Comparison of ground truth to the calculated distances revealed agreement to within 2% over 95% of the time. With the support of Joy Mining engineers, we connected this sensor to the on-board machine controller, and used it to perform a fully autonomous sump motion. The accuracy of this automated motion was also measured to be within 2%. The heading control system was also tested as both a miner aid and in a closed-loop system together with the controller. As a miner aid, the system consistently measured heading to within 0.3 degrees and offset to within 2 centimeters. As a closed-loop system, the miner was programmed to follow the course indicated by the laser. The steering control of our machine is somewhat unpredictable, largely due to the lack of damping typically provided by the coal face. Nonetheless, the system was able to follow this course with an error of under 5 centimeters. Both systems were also tested (in their miner aid instantiation) at the Tour-Ed mine. The sump depth system was tested for 10 sump motions, of which nine resulted in errors of under 2%. While we were unable to measure ground truth for the heading control system, the measured variance in heading and offset appeared similar to the results from our simulated mine, at distances of up to 80 meters from the laser. Testing of both systems were performed in conditions of heavy water and dust. The heading control system is largely unaffected by these problems, due to its ability to function purely off of the gyro for long periods of time. While extremely heavy dust can

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