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Robotics and AI as Enablers for Greener Dismantling, Remanufacturing and Recycling

Project description

AI-driven robotic systems for dismantling, remanufacturing and recycling

In recent years, the European circular economy is being shaped, presenting new opportunities and challenges in terms of materials reusing, waste processing, remanufacturing and refurbishing of products. Processing products after their (first) life requires high level of flexibility which is attributed to the different part conditions and variability. The EU-funded ROB4GREEN project will develop user-friendly, collaborative, AI-driven robotic systems capable of adapting to various R-strategies for processing products after their (first) lifecycle. These systems will enhance existing robot skills and generate new ones while operating autonomously. The ROB4GREEN solution will be validated at scale in major industries, showcasing optimisation ranging from the “cell” level to the whole “value chain”, thereby contributing to the goals of the European Green Deal.

Objective

During the last years, EU manufacturing has faced production flexibility challenges by deploying, among others, novel hybrid manufacturing systems, involving collaborative robots and mobile manipulators combined with flexible grippers, vision systems, sophisticated tasks/actions planning solutions and flexible integration platforms. Despite the importance of AI enabled flexible robotic systems, several aspects settle back their wider adoption, and impact on the objectives of the green deal:
•Limited cognition/ intelligence: existing solutions support non-trivial tasks but cannot act autonomously.
•Insufficient perception and diagnostics: In a circular economy, there is an increased need for understanding the state of products or parts that are being handled, after they have been used.
•Decision making is restricted: Current decision-making focuses on process or line level, not taking into account optimization at value chain level or per individual product.
•Small scale adaptation of AI due to small number of available data and training needed, to support tailored solutions in high variability context.
•Lack of use of explicitized knowledge in AI and robotics. Lifecycle data and knowledge is not used across the value chain to improve decision making after a product’s first life.
•Complexity in robot programming and interaction which requires the involvement of skilled engineers, does not provide flexibility in execution,


Thus, ROB4GREEN aims to develop easy to use and deploy AI driven collaborative robotic systems, that can reason and adapt to a variety of strategies for processing products after their first life, both hardware and behavior wise, improving existing skills and generating new ones, working autonomously combining data and knowledge. Such systems will be validated at scale and in major industries, showcasing optimization ranging from cell to the whole value chain, towards achieving significant impact on the objectives of the green deal.

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Keywords

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Programme(s)

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Topic(s)

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HORIZON-IA - HORIZON Innovation Actions

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Call for proposal

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(opens in new window) HORIZON-CL4-2024-DIGITAL-EMERGING-01

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Coordinator

PANEPISTIMIO PATRON
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 5 026 981,25
Address
UNIVERSITY CAMPUS RIO PATRAS
265 04 RIO PATRAS
Greece

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Region
Κεντρική Ελλάδα Δυτική Ελλάδα Αχαΐα
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 5 026 981,25

Participants (10)

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