Traditional manufacturing systems lack the necessary flexibility and reconfigurability that can allow short production cycles and fast deployment of the updated system. Although the use of automation technologies based on industrial robots can increase the adaptability of a production line, the desired flexibility cannot be achieved until abilities for genuine collaboration of the robots with the human workers are developed.

CoLLaboratE will revolutionize the way industrial robots learn to cooperate with human workers for performing new manufacturing tasks, with a special focus on the challenging area of assembly operations. The envisioned system for collaborative assembly will be capable of allocating human and robotic resources for executing the production plan sharing the tasks according to the capabilities of the available actors.

The CoLLaboratE project will build upon state-of-the-art methods for teaching the robot assembly tasks using human demonstration, extending them to facilitate genuine human-robot collaboration. To this end, a framework for equipping the robots and AGV mobile platforms with basic collaboration skills, such as load sharing, human touch recognition and human intention detection, will also be developed, coupled with deep reinforcement learning algorithms for increasing adaptability. Special attention will be paid to providing effective safety strategies allowing the use of a fenceless approach within the production cell. As a result, closer collaboration will be achievable and efficient production plans making optimal use of the available resources will be designed and executed.

The proposed solution will be evaluated in four different pilot sites, which will be implemented as collaborative factory floors of the industrial partners in Italy, Slovenia, Turkey, and Romania.

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

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Project Coordinator
Prof. Zoe Doulgeri
Automation & Robotics Lab
Aristotle University of Thessaloniki
Department of Electrical & Computer Engineering
Thessaloniki 54124, Greece
Collaborate Project CoLLaboratE Project
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