Knowledge Commons of Institute of Automation,CAS
Seam Feature Point Acquisition Based on Efficient Convolution Operator and Particle Filter in GMAW | |
Fan, Junfeng1,2![]() ![]() ![]() ![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
![]() |
ISSN | 1551-3203 |
2021-02-01 | |
卷号 | 17期号:2页码:1220-1230 |
摘要 | Seam feature point acquisition is the premise of the intelligent welding process such as initial point guiding and seam tracking. However, conventional seam feature point acquisition methods based on geometric feature have shortcomings of poor flexibility and robustness. In this article, a seam feature point acquisition method based on efficient convolution operator (ECO) and particle filter (PF) is proposed, which could be applied to different weld types and could achieve fast and accurate seam feature point acquisition even under the interference of welding arc light and spatter noises. First, a structured light vision sensor is developed to acquire welding image. Second, the ECO algorithm is adopted to track the seam region and acquire seam feature point during gas metal arc welding process. Third, the state and measurement equations of the weld seam position are established, and PF is applied to improve seam feature point acquisition accuracy. Finally, a welding experiment system is built and a series of seam feature point acquisition experiments of butt joint, lap joint, and fillet joint are carried out to validate the performance of the proposed method. The experiment results demonstrate that the processing speed of the proposed method could reach up 35x00A0;Hz, and the seam feature point acquisition errors are smaller than 0.15x00A0;mm, which could meet the real-time and accuracy requirement for subsequent initial point guiding and seam tracking. |
关键词 | Welding Target tracking Vision sensors Feature extraction Cameras Robots Convolution Efficient convolution operator (ECO) particle filter (PF) robot intelligent welding seam feature acquisition structured light vision |
DOI | 10.1109/TII.2020.2977121 |
关键词[WOS] | TRACKING ; SYSTEM |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[U1813208] ; National Natural Science Foundation of China[61903362] ; State Key Laboratory of Management and Control for Complex Systems[20190201] ; State Key Laboratory of Management and Control for Complex Systems[TII-20-0274] |
项目资助者 | National Natural Science Foundation of China ; State Key Laboratory of Management and Control for Complex Systems |
WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
WOS类目 | Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial |
WOS记录号 | WOS:000600967800030 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 智能机器人 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42750 |
专题 | 复杂系统认知与决策实验室_先进机器人 复杂系统认知与决策实验室_水下机器人 |
通讯作者 | Zhou, Chao |
作者单位 | 1.Chinese Acad Sci, Key Lab Complex Syst & Intelligence Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
推荐引用方式 GB/T 7714 | Fan, Junfeng,Deng, Sai,Ma, Yunkai,et al. Seam Feature Point Acquisition Based on Efficient Convolution Operator and Particle Filter in GMAW[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2021,17(2):1220-1230. |
APA | Fan, Junfeng,Deng, Sai,Ma, Yunkai,Zhou, Chao,Jing, Fengshui,&Tan, Min.(2021).Seam Feature Point Acquisition Based on Efficient Convolution Operator and Particle Filter in GMAW.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,17(2),1220-1230. |
MLA | Fan, Junfeng,et al."Seam Feature Point Acquisition Based on Efficient Convolution Operator and Particle Filter in GMAW".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 17.2(2021):1220-1230. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论