Vision-based Six-Dimensional Peg-in-Hole for Practical Connector Insertion

Kun Zhang1; Chen Wang2; Hua Chen3; Jia Pan2; Michael Yu Wang4; Wei Zhang3

10.1109/ICRA48891.2023.10161116

2023-05-29

IEEE International Conference on Robotics and Automation (ICRA)

1050-4729

2577-087X

979-8-3503-2366-5

2023 IEEE International Conference on Robotics and Automation (ICRA)

2023-May

1771-1777

29 May-2 June 2023

London, United Kingdom

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

We study six-dimensional (6D) perceptive peg-in-hole problem for practical connector insertion task in this paper. To enable the manipulator system to handle different types of pegs in complex environment, we develop a perceptive robotic assembly system that utilizes an in-hand RGB-D camera for peg-in-hole with multiple types of pegs. The proposed framework addresses the critical hole detection and pose estimation problem through combining the learning-based detection with model-based pose estimation strategies. By exploiting the structure of the peg-in-hole task, we consider a rectangle-shape based characterization for modeling the candidate socket. Such a characterization allows us to design simple learning-based methods to detect and estimate the 6D pose of the target socket that balances between processing speed and accuracy. To validate our method, we test the performance of the proposed perceptive peg-in-hole solution using a KUKA iiwa7 robotic arm to accomplish the socket insertion task with two types of practical sockets (RJ45/HDMI). Without the need of additional search, our method achieves an acceptable success rate in the connector insertion tasks. The results confirm the reliability of our method and show that our method is suitable for real world application.

Robotic assembly Learning systems Visualization Sockets Pose estimation Robot vision systems Manipulators

其他

英语

EI ; CPCI-S

Hong Kong Innovation and Technology Fund (ITF)[ITS/036/21FP]

Automation & Control Systems ; Computer Science ; Engineering ; Robotics

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Robotics

WOS:001036713001084

20233514632124

Learning systems ; Robotic assembly

Mechanical Design:601 ; Robot Applications:731.6

IEEE

1.Robotics Institute, Hong Kong University of Science and Technology, Hong Kong
2.Department of Computer Science, University of Hong Kong, Hong Kong SAR
3.Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China
4.HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China

Kun Zhang,Chen Wang,Hua Chen,et al. Vision-based Six-Dimensional Peg-in-Hole for Practical Connector Insertion[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:1771-1777.