凿岩机器人钻臂定位控制研究

发布时间：2018-01-13 14:23

本文关键词：凿岩机器人钻臂定位控制研究　出处：《江西理工大学》2017年硕士论文　论文类型：学位论文

【摘要】：钻爆法(钻孔、装药、爆破开挖岩石的方法)是井下采矿和隧道开挖普遍采用的施工方法。凿岩机器人具有高度自主操作能力,能有效改善人工半机械化凿岩的作业环境、降低工人的劳动强度,提高开采效率。其钻臂的定位精度和速度直接关系到岩层的钻爆精度,影响炮孔工艺性、工程效率、矿石的贫化率及利用率,是凿岩机器人领域重要的研究课题之一。地下采矿巷道和施工隧道狭窄而曲折,为保障作业的灵活性、避障性和操作性能,凿岩机器人普遍采用冗余多自由度关节耦合钻臂,增加了其定位控制运动学逆解的复杂性和难度,同时,也降低了求解效率和定位精度。针对凿岩机器人钻臂定位控制运动学求解、动力学参数辨识和定位误差补偿方法等,本文开展的主要研究工作如下:基于D-H方法建立钻臂运动学模型,推导出钻臂执行机构末端相对机身的正向运动学方程。提出一种交叉精英反向粒子群优化(CEOPSO)算法搜索钻臂目标位姿逆解。将交叉算子引入精英反向粒子群优化算法中,在维护粒子个体与最优解之间信息交换的基础上,增加粒子个体之间的信息交换,采用自适应惯性权重和交叉概率参数控制技术,提高算法的搜索能力和钻臂定位效率。仿真结果表明,CEOPSO算法迭代过程平稳,提高了钻臂定位控制性能,具有较好的工程应用价值。钻臂结构庞大,动力学模型参数辨识困难且精度低。采用牛顿-欧拉法建立钻臂动力学模型。为减少辨识过程因关节运动产生的冲击震荡,基于傅立叶级数规划钻臂运动关节轨迹。在CAD辨识法基础上,采用理论辨识法对钻臂各关节动力学参数进行分步辨识,为钻臂动力学的准确建模提供精确参数。建立包含参数误差和形变误差的钻臂定位误差模型。设计了一种5参数D-H钻臂模型。采用叠加法求解大臂、推进梁和翻转机构关节的挠度变形。通过引入一个虚拟关节,简化形变误差计算模型。应用CEOPSO算法搜索钻臂的定位误差补偿值,仿真结果表明所提出的算法能有效提高钻臂定位精度。
[Abstract]:Drilling and blasting method (drilling, charging, blasting rock excavation method) is widely used in underground mining and tunnel excavation. The drilling robot has a high degree of autonomous operation ability. It can effectively improve the working environment of artificial semi-mechanized rock drilling, reduce the labor intensity of workers, and improve the mining efficiency. The positioning accuracy and speed of the drilling arm are directly related to the drilling and blasting accuracy of rock strata and affect the technological characteristics of the blasting holes. Engineering efficiency, ore dilution rate and utilization ratio are one of the important research topics in the field of rock drilling robot. Underground mining roadway and construction tunnel are narrow and tortuous, in order to ensure the flexibility of operation, obstacle avoidance and operational performance. The redundant multi-degree-of-freedom joint coupling arm is widely used in rock drilling robot, which increases the complexity and difficulty of kinematics inverse solution of positioning control. It also reduces the solving efficiency and positioning accuracy. The kinematics solution, dynamic parameter identification and positioning error compensation method for drilling arm positioning control of rock drilling robot are also presented. The main work of this paper is as follows: based on D-H method, the kinematics model of drill arm is established. The forward kinematics equation of the end of the arm actuator relative to the fuselage is derived. A cross elite inverse particle swarm optimization (CEOPSO) is presented. The crossover operator is introduced into the elite inverse particle swarm optimization algorithm. On the basis of maintaining the information exchange between particle individual and optimal solution, the information exchange between particle individual is increased, and adaptive inertial weight and cross-probability parameter control technique are adopted. The simulation results show that the iterative process of the CEOPSO algorithm is stable, the performance of the drilling arm positioning control is improved, and it has good engineering application value. The structure of the drill arm is huge. The parameter identification of dynamic model is difficult and the precision is low. Newton-Euler method is used to establish the dynamic model of drill arm. In order to reduce the impact shock caused by joint motion in the identification process. Based on the Fourier series programming the trajectory of the moving joint of the drill arm. Based on the CAD identification method, the dynamic parameters of the drill arm joints are identified step by step by using the theory identification method. This paper provides accurate parameters for accurate modeling of drill arm dynamics, establishes the positioning error model of drill arm including parameter error and deformation error, and designs a 5-parameter D-H arm model. The superposition method is used to solve the large arm. By introducing a virtual joint, the deformation error calculation model is simplified, and the CEOPSO algorithm is used to search the position error compensation value of the drill arm. Simulation results show that the proposed algorithm can effectively improve the precision of arm positioning.
【学位授予单位】：江西理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP242

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