The SpinEye project incorporates a human-robotic collaboration, premised on AI vision-system, which detects screw positions, provides Cloud inrastructure for training, monitoring and enhancement of the detection model.
A robust specialised Artificial Intelligence (AI) scheme that has been extensively trained on the collected data and validated on various samples has been adopted. This module is responsible for detecting and accurately localising screw holes on PCBs. Additionally, a teach-in user friendly interface, the so-called URcap, has been implemented for simplifying the hand-eye calibration process. Finally, a SpinEye low-cost box that controls the communication interfaces between all the components has been included. Every day, millions of screws and bolts are mounted in the European manufacturing industry by employees with handheld screwdrivers in everything from windows to cars & electronic products. The Trinity demonstrator shows how to automate high/Mix – low/volume productions involving screw assembly task, by introducing an easy-to use camera system - SpinEye which is fast to setup and program. Customers that will deploy SpinEye and will benefit from faster changeover between tasks, while there is also no need for highly skilled workers to set up an assembly task. The quality rate will be increased, as the vision system will be able to detect slight mispositions of the fixture and adjust the cobot’s instructions.
Challenge
Using camera technology in assembly tasks reduce the time it takes to program and deploy cobots for screw driving tasks.
Impact
FSTP Name:
SPINEYE
Beneficiary Lead:
Spin Robotics
spin-robotics.com/
Denmark
Beneficiary 2:
D-CUBE
www.d-cube.eu/
Greece
Beneficiary 3:
Technology Area:
Human-Robot Collaboration
End User:
Electronic & Automotive industry and companies having final
Start Date - End Date:
01/11/2021 - 31/08/2022
Duration:
11 months
FSTP Funding:
125,016,00 €
TRL Level at Start:
4
TRL Level at End:
6
Number of early adopters raised:
1
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