[1]郭永强,崔春江,王 炜,等.基于深度学习的液体装卸车鹤管自动对接方法[J].机械与电子,2025,(06):75-80.
 GUO Yongqiang,CUI Chunjiang,WANG Wei,et al.Automatic Docking Method for Liquid Loading and Unloading Truck Crane Tubes Based on Deep Learning[J].Machinery & Electronics,2025,(06):75-80.
点击复制

基于深度学习的液体装卸车鹤管自动对接方法()
分享到:

《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年06期
页码:
75-80
栏目:
机电一体化
出版日期:
2025-06-27

文章信息/Info

Title:
Automatic Docking Method for Liquid Loading and Unloading Truck Crane Tubes Based on Deep Learning
文章编号:
1001-2257 ( 2025 ) 06-0075-06
作者:
郭永强崔春江王 炜张 强陈宗宇何 坤
长庆油田分公司第三采气厂,陕西 西安 710299
Author(s):
GUO Yongqiang CUI Chunjiang WANG Wei ZHANG Qiang CHEN Zongyu HE Kun
( The Third Gas Production Plant of Changqing Oilfield Branch , Xi ’ an 710299 , China )
关键词:
深度学习液体装卸车鹤管自动对接中心点标定缩放因子
Keywords:
deep learning liquid loading and unloading truck automatic docking of crane tubes center point calibration scale factor
分类号:
TP273 ;TH39
文献标志码:
A
摘要:
提出基于深度学习的液体装卸车鹤管自动对接方法。首先分析自动鹤管本体结构,采用九点标定法得到鹤管轴线与坐标系之间的夹角,完成鹤管静态中心点坐标获取;然后根据鹤管关节的运动参数,拟合鹤管运动轨迹;最后引入深度学习理论,基于构建的 RVFA-LSTM 模型完成鹤管对接,利用几何特征分析方法进行对接点缩放因子计算,完成罐口对接点的校正,实现液体装卸车鹤管与罐口的自动对接优化。实验结果表明,此方法对接精度高,对接平均绝对偏差低,能够满足工程要求。
Abstract:
An automatic docking method for liquid loading and unloading trunk crane tubes based on deep learning is proposed.Firstly , the structure of the automatic crane tube body is analyzed.The nine point calibration method is adopted to obtain the angle between the crane tube axis and the coordinate system , and the acquisition of the static center point coordinates of the crane tube is completed.Then , based on the motion parameters of the crane joint , the motion trajectory of the crane joint is fitted.Finally , deep learning theory is introduced to complete the docking of crane tubes by using the constructed RVFA LSTM model.Geometric feature analysis is employed to calculate the scale factor of the docking point , and the ground correction of the tank mouth docking point is executed , achieving automatic docking optimization between the crane tube and the tank mouth of the liquid loading and unloading truck.The experimental results show that this method has high docking accuracy and low average absolute deviation , which can meet the engineering requirements.

参考文献/References:

[ 1 ] 李哲,于海生,吴贺荣,等 . 基于新型 RRT 算法与视觉定位的机械臂工件抓取[ J ] . 组合机床与自动化加工技术,2022 ( 12 ): 148-153.

[ 2 ] 李鑫炎,周敏,张美洲,等 . 基于改进的 SURF 算法的机械臂识别定位及抓取研究[ J ] . 组合机床与自动化加工技术,2024 ( 1 ): 47-52.
[ 3 ] 薛雅丽,向心,杨皓文,等 . 基于改进 YOLOv3 的罐车底部接口识别[ J ] . 机械制造与自动化,2022 , 51 ( 4 ):166-168 , 199.
[ 4 ] CHEN Q , LI S , CHEN Q , et al.Multi-level information fusion enhanced by scene constraint : key to improve autonomous positioning accuracy in urban underground pipeline using MEMS inertial sensors [ J ] . Measurement , 2025 , 239 : 115442.
[ 5 ] 王殿君,高林林,陈亚,等 . 基于机器视觉定位的自动鹤管系统设计[ J ] . 机床与液压, 2023 , 51 ( 3 ): 130-135.
[ 6 ] 张彦泽,于斌超,马大智,等 . 自适应扩展卡尔曼滤波机械臂末端定位[ J ] . 组合机床与自动化加工技术, 2022( 10 ): 150-153 , 158.
[ 7 ] 胡宜洋,梁顺坤,关棒磊,等 . 振动干扰下非合作目标双目位姿测量方法[ J ] . 中国测试, 2025 , 51 ( 2 ): 132-139.
[ 8 ] 宗泽,张磊,贾志煦,等 . 目标物有定位偏差的三指机械手位姿调 整[ J ] . 组 合机 床与 自 动化 加工 技 术,2023( 8 ): 22-26 , 30.
[ 9 ] 袁靖肖,汪洋 . 基于统计学的小尺寸光点质心快速定位算法 [ J ] . 计算机仿真, 2022 , 39 ( 3 ): 407-412.
[ 10 ] 潘海鸿,喻洪基,陈旭红,等 . 用于超精密抛光机床的 Spline+GLS 定位误差补偿模型[ J ] . 组合机床与自动化加工技术, 2023 ( 5 ): 156-159 , 163.
[ 11 ] 张耀,吴一全,陈慧娴 . 基于深度学习的视觉同时定位与建图研究进展[ J ] . 仪器仪表学报, 2023 , 44 ( 7 ):214-241.
[ 12 ] 徐亚飞,王星雨,钟舜聪,等 . 基于稀疏表示的叶片热障涂层太赫兹检测回波信号定位分离方法[ J ] . 机械工程学报,2023 , 59 ( 8 ): 9-19.
[ 13 ] 董涛,杨宝华 . 基于多数据集深度学习的视觉传感图像目标增强识别[ J ] . 传感技术学报, 2024 , 37 ( 1 ): 64-70.
[ 14 ] 董慧芬,严力,孙浩远 . 基于单目视觉的机械臂灯具清洗定位系统[ J ] . 电子测量与仪器学报, 2022 , 36 ( 2 ):114-121.
[ 15 ] 陈甦欣,赵安宁,罗乐文 . 基于机器视觉的芯片字符区域分割和定位算法[ J ] . 组合机床与自动化加工技术,2024 ( 4 ): 10-13 , 18.

相似文献/References:

[1]王骁贤,张保华,陆思良.基于连续小波变换和卷积神经网络的无刷直流电机故障诊断[J].机械与电子,2018,(06):29.
 WANG Xiaoxian,ZHANG Baohua,LU Siliang.Fault Diagnosis of Brushless Direct Current Motor Based on Continuous Wavelet Transform and Convolutional Neural Network[J].Machinery & Electronics,2018,(06):29.
[2]刘志宇,黄亦翔.基于深度学习和迁移学习的液压泵健康评估方法[J].机械与电子,2018,(09):67.
 LIU Zhiyu,HUANG Yixiang.Health Assessment for Hydraulic Pump Based on Deep Learning and Transfer Learning[J].Machinery & Electronics,2018,(06):67.
[3]肖倩宏,康 鹏,杜 江,等.深度学习在电网智能调控系统中应用研究[J].机械与电子,2021,(01):38.
 XIAO Qianhong,KANG Peng,DU Jiang,et al.Research on the Application of Deep Learning Theory in Power Grid Intelligent Dispatching[J].Machinery & Electronics,2021,(06):38.
[4]许 哲,张少帅,郭 璐,等.无人机深度学习去雾算法[J].机械与电子,2021,(04):13.
 XU Zhe,ZHANG Shaoshuai,GUO Lu,et al. Deep Learning Defogging Algorithm for UAV[J].Machinery & Electronics,2021,(06):13.
[5]齐爱玲1,李 琳1,朱亦轩2,等.基于融合特征的双通道CNN滚动轴承故障识别[J].机械与电子,2021,(05):15.
 QI Ailing,LI Lin,ZHU Yixuan,et al.Dual Channel CNN Bearing Fault Identification Based on Fusion Feature[J].Machinery & Electronics,2021,(06):15.
[6]徐先峰,郑少杰,赵 依,等.基于数据分解与重构的光伏发电功率超短期预测[J].机械与电子,2022,(04):20.
 XU Xianfeng,ZHENG Shaojie,ZHAO Yi,et al.Ultra-short-term Prediction of Photovoltaic Power Generation Based on Data Decomposition and Deconstruction[J].Machinery & Electronics,2022,(06):20.
[7]江 励,熊达明,汤健华,等.自然光线环境中的空间物体快速识别和定位算法研究[J].机械与电子,2022,(06):8.
 JIANG Li,XIONG Daming,TANG Jianhua,et al.Recognition and Positioning Algorithm of Space Objects in Natural Light Environment[J].Machinery & Electronics,2022,(06):8.
[8]王西志,管声启,张理博,等.基于视觉引导的工业棒材上料系统研究[J].机械与电子,2023,41(05):19.
 WANG Xizhi,GUAN Shengqi,ZHANG Libo,et al.Research on Industrial Bar Feeding System Based on Visual Guidance[J].Machinery & Electronics,2023,41(06):19.
[9]王 青,吕绪山,党 帅,等.基于深度学习的纱管识别方法研究[J].机械与电子,2023,41(12):20.
 WANG Qing,LYU Xushan,DANG Shuai,et al.Research on Yarn Bobbin Detection Method Based on Deep Learning[J].Machinery & Electronics,2023,41(06):20.
[10]姜越夫,王 青,吕绪山.改进 YOLOv5s 的纱管目标检测方法[J].机械与电子,2024,42(02):29.
 JIANG Yuefu,WANG Qing,LYU Xushan.Improved YOLOv5s Method for Yarn Tube Object Detection[J].Machinery & Electronics,2024,42(06):29.

备注/Memo

备注/Memo:
收稿日期: 2024-11-08
基金项目:陕西省工业攻关计划资助项目( 2013K7-46 )
作者简介:郭永强 ( 1981- ),男,陕西咸阳人,高级工程师,研究方向为油气田开发、机械设计;崔春江 ( 1979- ),男,黑龙江齐齐哈尔人,高级工程师,研究方向为油气田开发、机械设计。
更新日期/Last Update: 2025-07-04