In-Hand Pose Estimation and Pin Inspection for Insertion of Through-Hole Components

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Abstract

The insertion of through-hole components is a difficult task. As the tolerances of the holes are very small, minor errors in the insertion will result in failures. These failures can damage components and will require manual intervention for recovery. Errors can occur both from imprecise object grasps and bent pins. Therefore, it is important that a system can accurately determine the object's position and reject components with bent pins.By utilizing the constraints inherent in the object grasp a method using template matching is able to obtain very precise pose estimates. Methods for pin-checking are also implemented, compared, and a successful method is shown.The set-up is performed automatically, with two novel contributions. A deep learning segmentation of the pins is performed and the inspection pose is found by simulation. From the inspection pose and the segmented pins, the templates for pose estimation and pin check are then generated.To train the deep learning method a dataset of segmented through-hole components is created. The network shows a 97.3 % accuracy on the test set. The pin-segmentation network is also tested on the insertion CAD models and successfully segment the pins.The complete system is tested on three different objects, and experiments show that the system is able to insert all objects successfully. Both by correcting in-hand grasp errors and rejecting objects with bent pins.

OriginalsprogEngelsk
Titel2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)
ForlagIEEE
Publikationsdato2022
Sider382-389
ISBN (Trykt)978-1-6654-9043-6
ISBN (Elektronisk)978-1-6654-9042-9
DOI
StatusUdgivet - 2022
Begivenhed18th IEEE International Conference on Automation Science and Engineering, CASE 2022 - Mexico City, Mexico
Varighed: 20. aug. 202224. aug. 2022

Konference

Konference18th IEEE International Conference on Automation Science and Engineering, CASE 2022
Land/OmrådeMexico
ByMexico City
Periode20/08/202224/08/2022
NavnIEEE International Conference on Automation Science and Engineering
Vol/bind2022-August
ISSN2161-8070

Bibliografisk note

Funding Information:
This project was funded in part by Innovation Fund Denmark through the project MADE FAST, in part by the SDU I4.0-Lab.

Publisher Copyright:
© 2022 IEEE.

Emneord

  • Computer vision
  • Pose estimation
  • Assembly

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