基于扩展卡尔曼滤波的清仓机器人位姿识别方法

李贵虎; 高贵军; 李军霞; 贾雪峰

doi:10.13272/j.issn.1671-251x.2024020004

基于扩展卡尔曼滤波的清仓机器人位姿识别方法

doi: 10.13272/j.issn.1671-251x.2024020004

李贵虎^{1, 2, 3,},
高贵军^{1, 2, 3, ,},
李军霞^{1, 2, 3},
贾雪峰^{1, 2, 3}

1.
太原理工大学机械与运载工程学院，山西太原　030024
2.
太原理工大学山西省矿山流体控制工程实验室，山西太原　030024
3.
太原理工大学矿山流体控制国家地方联合工程实验室，山西太原　030024

基金项目: 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）。

详细信息

作者简介:
李贵虎（1998—），男，山西原平人，硕士研究生，主要研究方向为煤矿机器人，E-mail：1304712930@qq.com

通讯作者:
高贵军（1973—），男，河北任县人，教授，博士，主要研究方向为矿山机械及其自动化，E-mail：gaogj161@163.com。

中图分类号: TD67
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出版历程
- 收稿日期: 2024-02-03
- 修回日期: 2024-05-15
- 网络出版日期: 2024-06-13

A pose recognition method for warehouse cleaning robots based on extended Kalman filtering

LI Guihu^{1, 2, 3
,},
GAO Guijun^{1, 2, 3
, ,},
LI Junxia^{1, 2, 3},
JIA Xuefeng^{1, 2, 3}

1.
College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2.
Shanxi Mine Fluid Control Engineering Laboratory, Taiyuan University of Technology, Taiyuan 030024, China
3.
National-local Joint Engineering Laboratory of Mining Fluid Control, Taiyuan University of Technology, Taiyuan 030024, China

摘要

摘要: 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。
- 煤矿水仓清理 /
- 清仓机器人 /
- 机器人位姿识别 /
- 多传感器融合 /
- 扩展卡尔曼滤波 /
- 煤泥自主清理
Abstract: 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.
- coal mine water storage cleaning /
- warehouse clearaning robot /
- robot pose recognition /
- multi sensor fusion /
- extended Kalman filtering /
- autonomous cleaning of coal slurry

HTML全文

图 1 清仓机器人位姿识别系统结构

Figure 1. Structure of pose recognition system for warehouse clearance robot

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图 2 清仓机器人上的传感器布置

1−激光雷达；2−算法控制箱； 3−惯性测量装置；4−里程计； 5−控制主板。

Figure 2. Sensor layout of warehouse cleaning robot

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图 3 清仓机器人里程计运动模型

Figure 3. Odometer motion model of warehouse cleaning

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图 4 EKF基本原理

Figure 4. Basic principle of extended Kalman filtering(EKF)

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图 5 多传感器融合算法原理

Figure 5. Multi-sensor fusion algorithm principle

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图 6 实验平台

1−激光雷达； 2−惯性测量装置；3−供电系统；4−核心控制系统； 5−里程计。

Figure 6. Experimental platform

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图 7 实验环境平面图

Figure 7. Experimental environment plan

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图 8 全局一致性地图

Figure 8. Globally consistent map

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图 9 沿直线方向位姿识别结果

Figure 9. Result of pose recognition along straight line

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图 10 巷道预设轨迹

Figure 10. Roadway preset trajectory

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表 1 坐标点实际值与2种方法处理结果

Table 1. Actual values of coordinate points and processing results using two methods m

观测点编号	真实坐标	测量坐标
观测点编号	真实坐标	AMCL	本文方法
1	（0.0, 0.0）	（−0.04, −0.03）	（−0.015, −0.003）
2	（−0.3, − 0.7）	（−0.326, −0.796）	（−0.316, −0.726）
3	（15, −0.4）	（15.105, −0.427）	（15.034, −0.423）
4	（10.3, −2）	（10.435, −1.877）	（10.289, −1.893）
5	（5, −0.85）	（5.105, −0.691）	（5.043, −0.821）

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表 2 姿态角实际值与2种方法处理结果

Table 2. Actual value of attitude angle and processing results using two methods rad

观测点编号	真实姿态角	测量姿态角
观测点编号	真实姿态角	AMCL	本文方法
1	1.45	1.428	1.463
2	−3.14	−3.054	−3.123
3	0.15	0.052	0.093
4	1.57	1.421	1.485
5	3.14	2.975	3.085

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表 3 位置误差和姿态角误差对比分析

Table 3. Comparative analysis of pose error and attitude angle error

编号	位置误差/m		姿态角误差/rad
编号	AMCL	本文方法	AMCL	本文方法
1	0.05	0.02	0.022	0.013
2	0.10	0.03	0.086	0.017
3	0.11	0.04	0.098	0.057
4	0.18	0.10	0.149	0.085
5	0.19	0.05	0.165	0.055

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