离子型稀土矿掘进选矿一体机掘进路径设计及避障研究

发布时间：2018-03-02 19:19

本文选题：离子型稀土矿掘进选矿一体机　切入点：避障　出处：《江西理工大学》2015年硕士论文　论文类型：学位论文

【摘要】：离子型稀土矿掘进选矿一体机具有一定的钻进能力,能够较好完成对离子型稀土矿的开采工作。在掘进过程中,掘进工艺路线确定方法可以分为两类:1、根据已有地质勘探资料分析所需开采的矿床分布,预先从地质图上确定出掘进工艺路线,然后用它来指导离子型稀土矿掘进选矿一体机的掘进过程。2、在没有明确地质勘探资料情况下,则需要在掘进前通过不全的地质资料来初步确定掘进路线,然后在实际掘进过程中,通过机载雷达获取的数据来校正掘进工艺路线。在离子型稀土矿掘进选矿一体机按掘进工艺路线进行掘进的过程中,经常会遇到非常坚硬的孤石体(普氏硬度?7),这些孤石体容易引起离子型稀土矿掘进选矿一体机开挖机构受力不均,刀具发生严重磨损,主轴承密封性破坏,进而导致刀盘发生堵塞,负载加大,烧毁电机等问题,最终导致离子型稀土矿掘进选矿一体机停留在矿山中,无法继续工作,从而需要对主要部件进行维修或替换,大量增加掘进过程的成本和时间,降低开挖过程的效率,所以对掘进过程进行避障研究具有重要意义。本文首先从矿山环境检测入手,采用地质雷达的地球物理探测方法来对障碍物进行检测。通过分析矿山中障碍物性质,对地质雷达参数如:中心频率等,进行了准确设定,从而识别出矿山环境中障碍物的大小和具体位置。然后运用相对定位的方法,通过离子型稀土矿掘进选矿一体机内部的直线位移、压力、倾角传感器和电子罗盘组成的位姿检测系统准确的定位离子型稀土矿掘进选矿一体机的空间位置和姿态,进而实现对装置的运动控制。运用栅格法原理对障碍物环境进行建模,然后运用蚁群算法对已建立的栅格矩阵进行路径规划仿真,验证蚁群算法运用到离子型稀土矿掘进选矿一体机的避障路径规划中具有可行性。针对不同的栅格模型,蚁群算法各参数的最优值有所区别,以三步走方式作为指导通过多次仿真实验得出了蚁群算法各参数的最优值。蚁群算法运用到避障路径规划中时存在收敛速度慢,全局寻优能力较差等问题,本文设计了如下改进方案:在进行栅格初始化时对凹形障碍物进行凸化处理;在初始信息素分配和概率转移公式中引入了节点到目标点的距离信息。最后对改进蚁群算法的仿真结果和基本蚁群算法最优结果进行对比,验证了改进蚁群算法在寻优能力和收敛速度上具有较大提升。
[Abstract]:Ion type rare earth ore driving-dressing machine has certain drilling ability and can complete the mining work of ion rare earth ore. The method of determining the technological route of tunneling can be divided into two categories: 1.According to the existing geological exploration data, we can analyze the distribution of the mineral deposits that need to be mined, and determine in advance the technological route of the excavation from the geological map. Then it is used to guide the tunneling process of the ion type rare earth ore driving-in-one machine. In the absence of clear geological exploration data, it is necessary to preliminarily determine the tunneling route through incomplete geological data before the excavation. Then in the actual tunneling process, the tunneling process is corrected by the data obtained by airborne radar. Very hard solitoids are often encountered (Prussian hardness? 7. These solitary stone bodies are apt to cause uneven force on the excavating mechanism of the ion type rare earth ore driving-mining integrated machine, serious wear of the cutting tool, destruction of the seal of the main bearing, and then lead to blockage of the cutter head, increase of the load, burning of the motor, and so on. As a result, the ion type rare earth ore tunneling machine stays in the mine and can not continue to work, so it is necessary to repair or replace the main parts, increase the cost and time of the tunneling process, and reduce the efficiency of the excavation process. Therefore, it is of great significance to study the obstacle avoidance in the process of tunneling. Firstly, this paper starts with mine environment detection and uses geophysical detection method of geological radar to detect obstacles. The parameters of GPR, such as center frequency, are accurately set to identify the size and location of obstacles in the mine environment, and then the relative positioning method is used. Through the linear displacement, pressure, inclination angle sensor and electronic compass, the position and attitude of the ion type rare earth ore drivage machine are accurately located, and the position and attitude of the ion type rare earth ore driving machine are accurately located. Then the motion control of the device is realized. The obstacle environment is modeled by the raster method, and then the path planning simulation of the established grid matrix is carried out by using the ant colony algorithm. To verify the feasibility of applying ant colony algorithm to the obstacle avoidance path planning of ion type rare earth ore tunneling machine. For different grid models, the optimum value of each parameter of ant colony algorithm is different. Under the guidance of the three-step approach, the optimum values of the parameters of the ant colony algorithm are obtained through many simulation experiments. When the ant colony algorithm is applied to obstacle avoidance path planning, the convergence rate is slow and the global optimization ability is poor. In this paper, the following improvement schemes are designed: the concave obstacle is convoluted when the grid is initialized; The distance information from the node to the target point is introduced into the initial pheromone assignment and probability transfer formula. Finally, the simulation results of the improved ant colony algorithm are compared with the optimal results of the basic ant colony algorithm. It is verified that the improved ant colony algorithm can improve the optimization ability and convergence speed.
【学位授予单位】：江西理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TD421.5

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