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.

• Type of Action HORIZON-RIA
• HORIZON-CL4-2023-RESILIENCE-01-02
• 1/1/2024-31/12/2027

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.