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Ambition

Revolutionize the way industrial robots learn to cooperate with human workers for performing assembly tasks.

Excellence

Equip robots with collaborative skills, using deep reinforcement learning algorithms and safety strategies.

Consortium

14 partners form the CoLLaboratE project, including universities, research institutes, SMEs and industries.

Impact

The excpected impact is to improve efficiency and flexibility by automatic assembly systems capable to rapidly learn new tasks

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Increase in job quality index

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Reduction in programming time/cost

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Reduction in reconfiguration time/cost
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In Romaero use case the robot can work collaboratively with a human technician. This way, percussive riveting becomes a semiautomated process. The system is flexible. It can change position and height, thus being usable for a variety of structures, different in size and shape.

In @KOLEKTOR_group the innovative technologies integrated into the work cell are: human intention recognition, dynamic task sharing, assembly of objects with low tolerances supported, automatic cell adaptation.

In @ArcelikGlobal use case the focus was on electronic card (PCB) assembly. The robot takes the PCB from the box and places the card in its place on the TV, then the operator screws the cards in place. This new collaborative cell presents good improvements in ergonomics.



This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820767.

The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.

Project Coordinator
Prof. Zoe Doulgeri
Automation & Robotics Lab
Aristotle University of Thessaloniki
Department of Electrical & Computer Engineering
Thessaloniki 54124, Greece
info(at)collaborate-project(dot)eu
Collaborate Project CoLLaboratE Project
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