A key foundation of the AGILEHAND project is the development of a comprehensive architectural framework that supports the full lifecycle of its technologies, ensuring sustainability, agility, and resilience in modern manufacturing environments. This framework provides the structural backbone that enables the integration of advanced sensing, robotic, and digital solutions into cohesive and interoperable production systems.
At the core of this effort is the AGILEHAND Reference Architecture (RA), which has been conceptualised in alignment with leading manufacturing reference architectures and relevant technical standards. This approach ensures compatibility, scalability, and interoperability across diverse industrial environments. The Reference Architecture defines how the different AGILEHAND components interact with each other and with external systems, enabling seamless integration and coordinated operation. This lesson explains how the AGILEHAND framework and Reference Architecture enable the design, integration, and deployment of the project’s innovative solutions. It provides essential knowledge to understand how complex manufacturing technologies can be structured, integrated, and managed to support agile, flexible, and human-centric production systems.
In this lesson, you will discover how smart sensing technologies are transforming the food handling and processing industry. These technologies allow production systems to automatically assess the quality of soft and deformable products—such as fish, meat, or fresh produce—in real time, without interrupting the production flow.
The AGILEHAND Smart Sensing Suite enables sensing systems to adapt easily to different products and production setups. Thanks to self-calibration capabilities, sensors can automatically adjust when the production line changes, ensuring continuous and reliable quality assessment. These solutions are designed to operate continuously in demanding industrial environments while remaining cost-effective and easy to adopt.
This lesson is organised into three parts. You will learn how sensors are integrated into a calibrated network, how internal and external product quality can be assessed, and how datasets are created and prepared to train intelligent grading systems.
By the end of this lesson, you will understand how smart sensing supports more flexible, efficient, and intelligent food processing systems.
In this lesson, you will discover how advanced robotic systems and intelligent physical platforms enable the safe and efficient handling, sorting, and packaging of soft and deformable products. These products are often fragile, variable in shape, and difficult to manipulate using conventional automation, requiring innovative and adaptive solutions.
The AGILEHAND project focuses on developing smart manipulation technologies and physical systems capable of interacting safely and effectively with delicate products. These systems are designed to adapt quickly to different product types and production requirements, enabling rapid reconfiguration of production lines while reducing the need for labor-intensive operations.
You will learn how intelligent robotic manipulators, adaptive grippers, and flexible physical platforms work together to autonomously grasp, sort, and package products. The lesson also highlights how these systems support safe and ergonomic collaboration between human workers and machines, creating more efficient and human-centric production environments.
In addition, you will explore how AGILEHAND integrates different hardware components through a common communication platform, ensuring coordination between robotic systems and production planning tools.
By the end of this lesson, you will understand how self-adaptive handling and packaging technologies contribute to more flexible, autonomous, and resilient manufacturing systems.
Agile, Flexible and Rapid Reconfigurable solutions
In this lesson, you will discover how digital technologies enable manufacturing systems to become more agile, flexible, and capable of rapidly adapting to changing production needs. Modern production environments must handle a wide variety of products, fluctuating demand, and evolving logistics requirements. To address these challenges, AGILEHAND develops intelligent digital solutions that support real-time monitoring, optimisation, and decision-making.
A key component of this approach is the implementation of product-oriented traceability solutions, which allow manufacturers to track products and production processes throughout the entire lifecycle. This improves transparency, quality control, and operational efficiency.
You will also learn how multi-layer digital twin systems create virtual representations of production environments, enabling simulation, monitoring, and analysis of manufacturing operations. These digital twins help identify inefficiencies, predict system behaviour, and support faster and more informed decisions.
In addition, this lesson introduces optimisation algorithms for production configuration and planning, designed to manage mixed-model production systems. These algorithms help coordinate production and logistics flows, ensuring efficient resource utilisation and enabling rapid reconfiguration of production lines in response to new requirements.
By the end of this lesson, you will understand how traceability, digital twins, and intelligent optimisation work together to support flexible, efficient, and resilient manufacturing systems aligned with the vision of Industry 5.0.