3 research outputs found
In-Hand Pose Estimation and Pin Inspection for Insertion of Through-Hole Components
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.Comment: 8 pages, 11 figures, 3 table