Benchmark Feature Detection Method for Mobile Robot Automatic Drilling System Integrated with Deep Learning

被引:0
作者
Dai, Jialong [1 ,2 ]
Shen, Jianxin [1 ]
Tian, Wei [1 ]
Li, Pengcheng [1 ]
Liu, He [3 ]
Cui, Xiangshun [2 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing 210016, Peoples R China
[2] Jiangsu AEROAPEX Intelligent Technol Co Ltd, Nanjing 210016, Peoples R China
[3] AVIC Jiangxi Hongdu Aviat Ind Grp Co Ltd, Nanchang 330096, Peoples R China
基金
中国国家自然科学基金;
关键词
automatic drilling; deep learning; YOLOv5; benchmark detection; robotics; mobile robotic systems;
D O I
10.3390/machines13020154
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Benchmark feature detection is critical in mobile robot automatic drilling systems for compensating robot accuracy and assembly errors in aerospace manufacturing. System accuracy is influenced by reference feature recognition, which is often hindered by material interference and background noise. To address these issues, this paper proposes a method that uses a 2D industrial camera for image capture, applies deep learning for initial target recognition and positioning, and then determines the feature extraction location based on the initial recognition. The extracted benchmark positions are accurately fitted using an improved Huber algorithm. Experimental results demonstrate that this approach improves the benchmark feature detection recognition rate by 43.8%, center recognition accuracy by 78.26%, and overall hole processing accuracy by 54.69%.
引用
收藏
页数:25
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