RESSORT Objectives

RESSORT addresses Europe’s urgent need for safer, adaptable, and sustainable robotic systems by advancing soft robotics from lab prototypes to real-world applications. Traditional rigid robots, while precise, lack the flexibility and safety required for human-centric environments. Soft robotics—built from compliant,
multifunctional materials—offers a transformative alternative, enabling safer human interaction, dynamic adaptability, and energy-efficient operation. Yet, Europe’s soft robotics sector remains underfunded and under-commercialized, with few solutions reaching high TRL levels.

RESSORT bridges this gap by integrating groundbreaking self-healing materials, variable-stiffness structures, and affordable soft 3D tactile sensing with AI-based control and multi-material additive manufacturing. The project will deliver three demonstrators: a patient-specific soft exosuit, a collaborative food-handling gripper/soft arm, and a morphing inspection drone. These systems will be validated at TRL≈7 in healthcare, food logistics, and industrial settings to enable wide spillovers.

To ensure market readiness, RESSORT embeds sustainability-aware design guidelines, certification pathways, and exploitation roadmaps. By retrofitting existing platforms with soft modules, the project accelerates adoption without infrastructure disruption. RESSORT aligns with EU priorities on safety, circularity, and European-developed AI and materials, contributing to the Digital Decade and EUGreendeal goals. Through coordinated dissemination and engagement, RESSORT will catalyze a new generation of resilient, efficient, and sustainable soft robotic technologies enabling unmet capabilities in robot systems.

• Type of Action: HORIZON-IA
• Call For Proposal: HORIZON-CL4-2025-04-DIGITAL-EMERGING-05
• Duration: 01/10/2026-30/09/2030

BOREAS Objectives

The BOREAS project aims to revolutionize how Europe secures vital raw materials for its green transition and technological independence. Europe is highly dependent on imports of magnesium and molybdenum, key elements for lightweight alloys in electric vehicles, renewable energy, and aerospace. Most magnesium comes from China, and Europe has no domestic molybdenum mining. This creates supply risks and vulnerability to global disruptions.

BOREAS focuses on the Malmbjerg project in Greenland, home to one of the world’s largest untapped molybdenum deposits, with significant magnesium by-products. However, mining in the Arctic is extremely challenging due to harsh weather, fragile ecosystems, and high costs. A major problem is ore contamination: iron oxides and sulfides coat valuable minerals, reducing recovery rates and
increasing environmental risks like acid rock drainage.

To address this, BOREAS will deploy fleets of autonomous aerial and ground robots equipped with advanced sensors. These robots will map, monitor, and selectively remove contaminant layers before they degrade ore quality. All data will feed into a real-time “digital twin”, a virtual model of the mine that predicts contamination, guides operations, and ensures environmental compliance.

The project embeds sustainability and transparency at every step, using real-time environmental monitoring and life cycle assessment to minimize impact and build trust with stakeholders.
By piloting these technologies at Malmbjerg, BOREAS will set a new standard for sustainable, traceable, and efficient mining in sensitive environments. The goal is to secure a reliable European supply of critical materials, reduce dependence on imports, and demonstrate a model for responsible resource extraction that can be replicated worldwide.

Website: https://www.boreas-mining.eu/

Project’s Linkedin Page: https://www.linkedin.com/company/boreas-mining/posts/?feedView=all

• Type of Action: HORIZON-IA
• Call For Proposal: HORIZON-CL4-INDUSTRY-2025-01-MATERIALS-62
• Duration: 1/05/2026-30/04/2030

Exploration drilling for a sustainable future!

Exploration drilling is often hampered by inefficiencies and high environmental costs, making it challenging to locate the critical raw materials needed for modern industries. Traditional methods can be slow and ineffective, leading to increased resource depletion and ecological disruption. As demand for these materials grows, finding innovative solutions becomes crucial. With this in mind, the EU-funded MINOTAUR project will investigate exploration drilling from a revolutionary perspective. MINOTAUR integrates geological principles with advanced technologies such as robotics, digital twin technology, and artificial intelligence. By treating exploration as a resource-mapping challenge, it enhances decision-making and streamlines geological assessments, ultimately paving the way for more efficient and sustainable resource management.

MINOTAUR Objectives

MINOTAUR seeks to address the exploration drilling problem from a novel and revolutionary perspective, bringing together geological principles and concepts involved in exploration drilling with modern technologies such as robotics, digital twin technology, and artificial intelligence. Along the way, MINOTAUR proposes novel research directions in allied areas like geophysics, sensing technologies in geology, environmental impact modeling of whole mineral and production systems, numerical modeling of entire mineral systems related to critical raw materials, new drilling techniques, high-resolution sensing technologies, and artificial intelligence with novel data processing tools for a robust and expeditious geological assessment.

MINOTAUR takes a fundamentally fresh look at exploration drilling and approaches it as a source seeking and resourcing mapping problem, drawing inspirations from solutions in robotics, but where geology is a central part of the decision, with the outcomes of exploration enhanced through artificial intelligence and digital twin technology.

Website: https://www.minotaur-mining.eu/
Project’s Linkedin Page: https://www.linkedin.com/company/minotaur-mining/posts/?feedView=all

• Type of Action: HORIZON-RIA
• Call For Proposal: HORIZON-CL4-2024-RESILIENCE-01
• Duration: 1/11/2024-31/10/2027

The Aim

The continuous effort and increased demand of the raw materials are directing the mining companies to excavate minerals at greater depths. This trend is challenging the current mining operations and the existing traditional technologies towards the objective to retain profitability, while achieving the latest Green Deal environmental vision and securing human workers safety. A key enabler, to address these challenges and to foster a sustainable development of the mining industry, is the development and deployment of innovative technologies for resource efficient extraction of the EU’s raw materials, as well as near mine exploration of critical raw materials in currently non-extracted ore bodies in existing or abandoned ones.

PERSEPHONE is aiming to address these challenges by developing of the pioneering technologies for pushing the limits of EU mining industry and embodiment of autonomous and integrated near mine exploration capability to access deep deposits of critical raw materials through hard-to-reach deep and abandoned mines. The overall concept and vision of PERSEPHONE will be achieved by reducing the size of mining machines currently adapted to the human scale and embedding autonomy for risk-aware navigation and full digitalization of the extraction process by digital twin creation and key enabling technologies validation at TRL 5. Additionally, PERSEPHONE is introducing completely novel approaches in online near mine exploration core analysis and overall integration of related data analytics to the mine expansion. Thus, PERSEPHONE allows to foster green transition by reducing the cost and waste generated from deep-mining operations and foster the vision of zero human presence in highly hazardous areas. These will allow to achieve PERSEPHONE’s overall goal to digitalize and automate extraction value chain by creation of new concepts of energy-efficient autonomous drilling machines with advanced perception capabilities for navigation, face drilling, and core extraction, which will enable data-driven digital twin creation and geological modelling for further enhanced decision support and optimal extraction planning.

Website: https://www.persephone-mining.eu/

Project’s Linkedin Page: https://www.linkedin.com/company/persephone-mining/posts/?feedView=all

• Type of Action: HORIZON-RIA
• Call For Proposal: HORIZON-CL4-2023-RESILIENCE-01-02
• Duration: 1/1/2024-31/12/2026

Four Universities and two industrial partners are involved in this project (U.W.C., U.R., T.U.B., U.P., Fazan, Image).

Development of an Expert System in designing of grippers for handling non-rigid materials.

The main aim of this research is to provide users of robots with a systematic approach for the design of grippers for handling non-rigid materials. The expert system will be implemented in a knowledge-based system called HOMER.

The objectives of the research are:

• To address specific non-rigid materials handling tasks which are difficult to automate and propose engineering solutions. The tasks will be specified by industrial partners.
• To develop an expert system for the design of grippers for handling non-rigid materials.
• To develop new grasping techniques for non-rigid materials.
• To improve current gripper design techniques specifically for the handling of non-rigid materials.
• To verify the developed knowledge-based system at the industrial partners’ site to prove its applicability and adaptability in real-world manufacturing environments

The Robotics Group of the University of Patras is interested in the following three tasks:

• Development of a Knowledge-based System for the generation of assembly strategies.
• Simulation of robot cells for assembly of a valve produced by Zita Ltd.
• Development of a Hybrid Force/Position Controller for the implementation of assembly strategies.

Knowledge Based System

• The knowledge-based system accepts as input IGES files where the parts are containing geometrical data for the parts to be assembled.
• The parts are classified according to their Geometric Features.
• The parts for each assembly task are classified as either moving or stationary.
• The C-Frames is defined on the moving part.
• The artificial constraints of each assembly subtask are formulated according to the contact case between the moving and the stationary part.

A special algorithm for the identification of the contact case is developed. This algorithm inputs the signal from the force sensor. In addition, a fuzzy decision system has been developed in order to reason about the feasibility of the assembly with respect to the tolerances between the two parts and the robot repeatability error.