悬臂式掘进机位姿视觉检测系统改进

张旭辉; 王恒; 沈奇峰; 杨文娟; 张超

doi:10.13272/j.issn.1671-251x.2021100051

悬臂式掘进机位姿视觉检测系统改进

doi: 10.13272/j.issn.1671-251x.2021100051

张旭辉^{1, 2,},
王恒^1, ,,
沈奇峰¹,
杨文娟^{1, 2},
张超¹

1.
西安科技大学机械工程学院, 陕西西安　710054
2.
陕西省矿山机电装备智能监测重点实验室, 陕西西安　710054

基金项目: 国家自然科学基金青年项目(52104166)；陕煤联合基金项目(2021JLM-03)；陕西省重点研发计划项目(2018ZDCXL-GY-06-04)。

详细信息

作者简介:
张旭辉(1972—)，男，陕西凤翔人，教授，博士，研究方向为煤矿机电设备智能检测与控制，E-mail：zhangxh@xust.edu.cn

通讯作者:
王恒（1997—），男，陕西咸阳人，硕士研究生，主要研究方向为智能检测与控制、视觉定位，E-mail：wh9726@163.com

中图分类号: TD632
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出版历程
- 收稿日期: 2021-10-30
- 修回日期: 2022-05-08
- 网络出版日期: 2022-05-19

Improvement of position and posture measurement system for boom-type roadheader based on machine vision

ZHANG Xuhui^{1, 2
,},
WANG Heng^{1
, ,},
SHEN Qifeng¹,
YANG Wenjuan^{1, 2},
ZHANG Chao¹

1.
College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China
2.
Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring, Xi'an 710054, China

摘要

摘要: 煤矿井下粉尘浓度高、照度低，图像采集质量和特征提取效果受粉尘浓度影响较大，而相机参数和图像处理参数不能根据粉尘浓度变化自适应调整，易产生点−线特征提取不稳定和图像丢帧等问题。针对上述问题，对掘进机位姿视觉检测系统进行改进，利用矿用防爆工业相机采集不同粉尘浓度下的激光点−线图像，通过透过率建立图像灰度值与粉尘浓度等级的关系模型，通过实验获取不同粉尘浓度等级下的最优相机参数和图像处理参数；提出一种参数自适应调整算法，根据粉尘浓度等级自适应调整参数值，提高图像采集质量和点−线特征提取的稳定性和精度，进而提高掘进机位姿视觉检测系统的精度。实验结果表明：改进后悬臂式掘进机位姿视觉检测系统在X，Y，Z方向的平均测量误差分别为28.26，30.58，22.54 mm，处理100张图像后得到的可用图像从75张提高到90张，说明参数自适应调整算法有效提高了图像特征提取精度和数据可用性，从而保证了悬臂式掘进机位姿视觉检测系统的精度和稳定性。
- 悬臂式掘进机 /
- 位姿检测 /
- 机器视觉 /
- 参数自适应 /
- 特征提取 /
- 粉尘浓度
Abstract: In coal mine, the dust concentration is high and the illumination is low. The image acquisition quality and characteristic extraction effect are greatly affected by dust concentration. However, the camera parameters and image processing parameters cannot be adjusted adaptively according to the change of dust concentration. Therefore, it is easy to cause problems such as unstable point-line characteristic extraction and image frame loss. In order to solve the above problems, the position and posture measurement system for boom-type roadheader based on machine vision is improved. The mine-used explosion-proof industrial camera is used to collect the laser point-line images under different dust concentrations. The relationship model between the image gray value and the dust concentration level is established through the transmittance. The optimal camera parameters and image processing parameters under different dust concentration levels are obtained through experiments. A parameter adaptive adjustment algorithm is proposed, and the parameter values are adjusted adaptively according to the dust concentration levels. Therefore, the image collection quality and the stability and precision of the point-line characteristic extraction are improved. Moreover, the precision of position and posture measurement system for roadheader based on machine vision is improved. The experimental result show that the average measurement errors in X, Y and Z directions of the improved vision detection system for boom-type roadheader are 28.26 mm, 30.58 mm and 22.54 mm respectively. The number of usable images is increased from 75 to 90 after processing 100 images. These results show that the parameter adaptive adjustment algorithm can effectively improve the precision of image characteristic extraction and the data availability. The algorithm ensures the precision and stability of position and posture measurement system for boom-type roadheader based on machine vision.
- boom-type roadheader /
- posture detection /
- machine vision /
- parameter adaptation /
- characteristic extraction /
- dust concentration

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图 1 悬臂式掘进机位姿视觉检测系统

Figure 1. Position and posture measurement system for boom-type roadheader based on machine vision

下载: 全尺寸图片幻灯片

图 2 改进后悬臂式掘进机位姿视觉检测系统流程

Figure 2. Flow of the improved position and posture measurement system for boom-type roadheader based on machine vision

下载: 全尺寸图片幻灯片

图 3 悬臂式掘进机位姿视觉检测系统实验平台

Figure 3. Experimental platform for position and posture measurement system for boom-type roadheader based on machine vision

下载: 全尺寸图片幻灯片

图 4 不同粉尘浓度等级下的激光束图像

Figure 4. Laser beam images under different dust concentration levels

下载: 全尺寸图片幻灯片

图 5 掘进机位姿测量误差

Figure 5. The measurement error of the position and posture of the roadheader

下载: 全尺寸图片幻灯片

表 1 图像灰度值与粉尘浓度等级的关系

Table 1. The relationship between image gray value and dust concentration level

灰度值	透过率/%	粉尘浓度等级
160~200	0~20	超高浓度
120~160	20~40	高浓度
70~120	40~70	中浓度
40~70	70~100	低浓度

下载: 导出CSV

表 2 中浓度粉尘环境下参数选取实验结果

Table 2. Experimental results of parameter selection in medium-concentration dust environment

曝光时间/μs	S_min	V_min	测量值/mm			可用图像/张
曝光时间/μs	S_min	V_min	X	Y	Z	可用图像/张
40 000	25	25	—	—	—	—
		35	—	—	—	—
		45	−124.3	9964.1	112.3	65
	35	25	−123.3	9959.2	116.3	72
		35	−125.2	9962.5	115.1	76
		45	−128.1	9969.3	113.3	91
	45	25	−125.4	9965.3	110.7	78
		35	−126.8	9968.1	112.1	88
		45	—	—	—	—
60 000	25	25	—	—	—	—
		35	—	—	—	—
		45	−125.4	9964.3	110.7	78
	35	25	−122.3	9965.6	110.3	80
		35	−126.2	9967.8	112.1	84
		45	−124.1	9966.6	114.3	88
	45	25	−126.4	9967.5	115.7	90
		35	−126.1	9969.2	113.1	94
		45	—	—	—	—
80 000	25	25	—	—	—	—
		35	—	—	—	—
		45	—	—	—	—
	35	25	—	—	—	—
		35	−126.2	9965.5	112.1	75
		45	−127.9	9961.2	115.3	79
	45	25	—	—	—	—
		35	−127.8	9963.1	111.1	84
		45	−129.1	9970.3	114.3	80

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表 3 不同粉尘浓度等级下的最优相机和图像处理参数

Table 3. Optimal camera and image processing parameters under different dust concentration levels

粉尘浓度等级	曝光时间/μs	S_min	V_min
高浓度	60 000	45	35，45
中浓度	60 000	35，45	35
低浓度	40 000	35	45

下载: 导出CSV

表 4 掘进机位姿检测实验结果

Table 4. The experimental results of the position and posture detection of the roadheader

项目	编号	X/mm	Y/mm	Z/mm
位姿真实值	1	−100	10 000	−90
	2	−200	15 000	−90
	3	−300	20 000	−90
	4	−350	25 000	−90
	5	−420	30 000	−90
位姿测量值（非自适应调整）	1	−135.2	9 963.8	−120.6
	2	−169.4	15 039.5	−60.9
	3	−339.8	20 039.2	−118.8
	4	−312.4	24 958.5	−60.4
	5	−458.5	30 042.2	−59.2
位姿测量值（自适应调整）	1	−128.1	9 969.3	−113.3
	2	−173.6	15 031.2	−67.5
	3	−329.5	20 029.7	−112.1
	4	−321.9	24 970.5	−68.9
	5	−449.2	30 031.8	−66.3

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表 5 参数自适应与非自适应调整算法误差对比

Table 5. Error comparison between parameter adaptive adjustment algorithm and non-adaptive adjustment algorithm

参数调整算法	最大误差/mm			平均误差/mm			可用图像/张
参数调整算法	X 方向	Y 方向	Z 方向	X 方向	Y 方向	Z 方向	可用图像/张
非自适应	39.8	42.2	30.8	36.34	39.74	29.78	75
自适应	29.5	31.8	23.7	28.26	30.58	22.54	90

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