College of Mechanical and Vehicle Engineering, Taiyuan University of Technology
Shanxi Mine Fluid Control Engineering Laboratory, Taiyuan University of Technology
National-local Joint Engineering Laboratory of Mining Fluid Control, Taiyuan University of Technology

煤矿水仓巷道光照强度不均匀且结构化特征明显，传统基于视觉的机器人位姿识别方法识别不准确，而单一的机器人定位技术如自适应蒙特卡洛（AMCL）方法随着清仓机器人的长时间运行，输出的位姿信息存在较大累计误差，易出现煤泥清理不干净、与两侧巷道发生碰撞的情况。针对上述问题，提出了一种基于扩展卡尔曼滤波的多传感器融合清仓机器人位姿识别方法。首先搭建多传感器融合算法框架，建立里程计、惯性测量装置、激光雷达数据采集模型；其次基于扩展卡尔曼滤波原理，以惯性测量装置角度信息建立观测方程，结合里程计位姿信息，得到第1次融合的清仓机器人位姿矩阵，利用激光雷达的位置信息与之前的位姿矩阵进行迭代，得到第2次融合的清仓机器人位姿矩阵；最后采用互补滤波算法对融合后的清仓机器人位姿矩阵进行处理，输出最终的清仓机器人位姿矩阵。实验结果表明：在直线位姿识别中2次的最大位置误差为0.04 m，最大姿态角误差为0.05 rad；在模拟巷道实验中的最大位置误差为0.1 m，最大姿态角误差为0.085 rad；与AMCL方法相比，基于扩展卡尔曼滤波的清仓机器人位姿识别方法在减少清仓机器人运行过程中的累计误差方面表现出显著的有效性。

The lighting intensity of coal mine water storage roadways is uneven and the structured features are obvious. Traditional vision based robot pose recognition methods are not accurate. The single robot positioning techniques such as Adaptive Monte Carlo localization (AMCL) method have significant cumulative errors in the output pose information with the long-term operation of the cleaning robot. It is easy to encounter situations where the coal slurry is not cleaned thoroughly and collides with both sides of the roadway. In order to solve the above problem, a multi-sensor fusion clearance robot pose recognition method based on extended Kalman filtering is proposed. Firstly, the method builds a multi-sensor fusion algorithm framework and establishes models for odometer, inertial measurement devices, and LiDAR data acquisition. Secondly, based on the principle of extended Kalman filtering, an observation equation is established using the angle information of the inertial measurement device. Combined with the odometer pose information, the first fusion of the clearance robot pose matrix is obtained. Then, the position information of the lidar is iterated with the previous pose matrix to obtain the second fused clearance robot pose matrix. Finally, the complementary filtering algorithm is used to process the pose matrix of the clearance robot after two fusion and output the final pose matrix of the clearance robot. The experimental results show that the maximum position error in linear pose recognition is 0.04 m, and the maximum attitude angle error is 0.05 rad. The maximum position error in the simulated roadway experiment is 0.1 m, and the maximum attitude angle error is 0.085 rad. Compared with the AMCL method, the pose recognition method of the warehouse cleaning robot based on extended Kalman filtering shows significant effectiveness in reducing the cumulative error during the operation of the warehouse cleaning robot.

山西省科技合作交流专项项目（202104041101005）；山西省重点研发计划项目（202102100401004）。

李贵虎，高贵军，李军霞，等. 基于扩展卡尔曼滤波的清仓机器人位姿识别方法[J]. 工矿自动化，2024，50（5）：99-106.

LI Guihu, GAO Guijun, LI Junxia, et al. A pose recognition method for warehouse cleaning robots based on extended Kalman filtering[J]. Journal of Mine Automation，2024，50（5）：99-106.