矿用智能巡检机器人无标定视觉伺服控制研究  

Research on uncalibrated visual servo control of mine intelligent inspection robot

作　　者：李静 黄友锐[1] 韩涛[1] 兰世豪 陈宏茂 甘福宝 LI Jing;HUANG Yourui;HAN Tao;LAN Shihao;CHEN Hongmao;GAN Fubao(School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China)

机构地区：[1]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《工矿自动化》2021年第11期30-39,共10页Journal Of Mine Automation

基　　金：国家自然科学基金项目(61772033)。

摘　　要：针对矿用智能巡检机器人无标定视觉伺服控制中采用基于传统的卡尔曼滤波(KF)的图像雅可比矩阵存在估计值不准确、鲁棒性差的问题,提出了一种具有长短期记忆(LSTM)的卡尔曼滤波算法(KFLSTM算法)。KFLSTM算法使用LSTM弥补由KF算法产生的估计误差,将滤波增益误差、状态估计向量误差、观测误差用于LSTM的在线训练,利用训练后的LSTM模型对雅可比矩阵进行最优估计,通过提高雅可比矩阵估计值的准确性和稳定性来改善视觉伺服控制的实时性和鲁棒性。建立了基于KFLSTM算法的无标定视觉伺服模型,将最优雅可比矩阵作为控制器的输入,使控制器输出较准确的关节角速度,从而控制机械臂的实时运行。将KFLSTM算法应用到矿用智能巡检机器人六自由度视觉伺服仿真实验中,结果表明:应用KFLSTM算法得到的图像误差收敛速度相较于传统KF算法提高了100%~142%,图像特征误差更小,定位精度为0.5像素,且机器人末端执行器运动平稳,具有较强的抗噪声干扰能力,可有效提高矿用智能巡检机器人的作业精度与效率,并增强其工作的稳定性与安全性。In order to solve the problem of inaccurate estimation and poor robustness of image Jacobian matrix based on traditional Kalman filtering(KF)in the uncalibrated visual servo control of mine intelligent inspection robot,Kalman filtering algorithm with long and short-term memory(LSTM)(KFLSTM algorithm)is proposed.The KFLSTM algorithm uses the LSTM to compensate for the estimation error generated by the KF algorithm,uses the filter gain error,state estimation vector error and observation error for online training of the LSTM,and uses the trained LSTM model for optimal estimation of the Jacobian matrix to improve the real-time and robustness of visual servo control by improving the accuracy and stability of the Jacobian matrix estimation.The uncalibrated visual servo model based on the KFLSTM algorithm is established,and the most optimal Jacobian matrix is used as the input of the controller,which makes the controller output more accurate joint angular velocity so as to control the real-time operation of the manipulator.The KFLSTM algorithm is applied to the six-degree-of-freedom visual servo simulation experiment of the mine intelligent inspection robot.The results show that the image error convergence speed obtained by the KFLSTM algorithm is 100%-142%higher than that of the traditional KF algorithm,the image characteristic error is smaller,the positioning precision is 0.5 pixels,and the robot end effector moves smoothly.Moreover,the method has strong anti-noise interference capability,which can improve the precision and efficiency of the mine intelligent inspection robot effectively and enhance its stability and safety.

关 键 词：矿用智能巡检机器人 无标定视觉伺服 图像雅可比矩阵 卡尔曼滤波 长短期记忆神经网络 

分 类 号：TD67[矿业工程—矿山机电]