The Digital, Industry and Space sector is a living ecosystem in Europe, where Horizon Europe projects do not work in silos but aim to learn from each other and create synergies that will take their impact to a higher level.
AID4SME is privileged to have been funded alongside five other projects: ROB4GREEN, iBot4CRMs, COSMIC, AEROSUB, and AMALTEA. Together, these projects form a cluster that will focus on creating synergies in exploitation, dissemination, and communication activities, the exchange of knowledge, and overcoming common challenges.
To maximise the visibility of these projects, AID4SME has been working on getting to know them better. As a result, ROB4GREEN coordinator, Dionisis Andronas, has kindly agreed to an interview to present his project, share an example of a recycling solution in the manufacturing industry, discuss the challenges of the circular economy, and highlight common opportunities for both ROB4GREEN and AID4SME (and how they can cooperate). Don’t miss this insightful piece!
Coordinator profile
Dionisis Andronas holds a Master’s degree in Mechanical Engineering, having graduated with highest honors from the Department of Mechanical Engineering and Aeronautics at the University of Patras. He is currently employed as a Senior Research Engineer and Project Manager at the Laboratory for Manufacturing Systems and Automation (LMS). During his studies, he was awarded a scholarship by the “Andreas Mentzelopoulos” Foundation, which was explicitly extended in recognition of his high academic performance and scientific merit.
Over the past decade, he has been actively involved in numerous European-funded projects, undertaking roles in research and development, technical management, and project coordination. His work has focused on addressing challenges across various (re-)manufacturing sectors through robotic automation, human-robot collaboration, and reconfigurable manufacturing systems, including the physical implementation of robotic cells in European factories. His contributions are demonstrated in multiple publications in prestigious scientific journals and conferences. Additionally, he serves as a reviewer for several prominent scientific journals and international conferences in the fields of robotics and manufacturing automation.
Could you explain the ROB4GREEN project in a nutshell?
ROB4GREEN is a Horizon Europe Innovation Action aimed at developing easy-to-use, deployable, and AI-driven collaborative robotic systems. The project brings together a highly competent and comprehensive consortium of 11 partners from 8 countries, coordinated by LMS. Technically, the project focuses on ‘Re-X’ strategies—specifically dismantling, remanufacturing, and recycling for products that have reached the end of their life cycle.
By combining advanced perception, autonomous reasoning, and innovative hardware, ROB4GREEN seeks to transform labour-intensive manual processes into automated, efficient circular workflows. The portfolio of enabling technologies, starting at the cell level and reaching up to the value chain level, is expected to have a significant impact on European economic and green objectives.
The technologies and integrated systems are planned to be validated through large-scale pilots in three key industrial sectors: energy infrastructure (wind turbine blades), automotive (tire retreading), and electronics (PCB remanufacturing). The validation activities and project impact will be further supported by its FSTP program of €4 million, which will attract SMEs and industrial partners for external piloting and co-development activities.
Give an example of a dismantling/remanufacturing/recycling solution that already exists and its limitations for the circular economy.
A primary example within ROB4GREEN is the Michelin use case for vehicle tire retreading. Tire retreading has been an industrial practice for many years and has the potential to reduce raw material use and retain value without following more energy-intensive circular loops. Currently, the retreading process for large vehicle tires is highly manual and depends on the tactile expertise of trained operators. Operators must manually inspect tires for defects, perform repairs using power tools, and manually manage the filling process before the new tread is added.
Our project aims to introduce AI-based machine vision to automatically identify defects and damage on the tire surface. Collaborative robots are then utilised to undertake ergonomically demanding tasks like tire skiving and repair, guided by a Smart Digital Twin and data from Digital Product Passports (DPP). These assets, combined with AI-based decision-making and large language models, can assist operators in classifying tires based on their defect history records, reducing worker training and upskilling periods.
A significant barrier is the high variability of used products; currently, automated systems struggle to adapt to the unique wear patterns of every individual tire. Without precise lifecycle data (which the DPP aims to solve), it is difficult for robots to act autonomously or decide whether a tire should be retreaded or recycled.
Why is the circular economy still challenging today in manufacturing?
The circular economy remains challenging in manufacturing today due to several entrenched barriers that prevent the widespread adoption of circular principles. A primary obstacle is limited cognition and intelligence; while current robotic solutions can handle repetitive tasks, they often lack the autonomy required to adapt to the unpredictable and highly variable state of used parts. This is further complicated by insufficient perception and diagnostics, as it remains technically difficult to automate the assessment of a product’s internal and external condition—such as identifying structural cracks in wind turbine blades—after years of use.
Beyond technical hardware limitations, data fragmentation acts as a major bottleneck, as lifecycle data is rarely shared across the value chain. This leaves decision-makers at the end-of-life stage without critical information regarding a product’s original specifications or usage history. Consequently, this leads to restricted decision-making, where optimisation is typically confined to the individual process or production line rather than the entire value chain, often resulting in suboptimal choices for material recovery. Finally, the complexity of programming and adapting AI and robotics to these high-variability contexts currently requires highly skilled engineers, making the transition to circularity cost-prohibitive for many SMEs.
What are the potential opportunities and touchpoints between ROB4GREEN and AID4SME?
As sister projects under the same Horizon Europe topic (HORIZON-CL4-2024-DIGITAL-EMERGING-01-04), ROB4GREEN and AID4SME share several strategic touchpoints that offer significant potential for synergy. Building on the recently established pathway for collaboration, ROB4GREEN, AID4SME, and the other sister projects can support each other to maximise their collective impact.
Technically, key areas for collaboration include the development of standardised data spaces, the identification of open communication architectures, and the communication of challenges that manufacturers, users, OEMs, and third-party remanufacturers face in technical and legislative terms.
Beyond technical alignment, there are extensive opportunities for joint dissemination, open calls, and stakeholder engagement, fostering a more robust European innovation landscape for circular manufacturing.