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multipurpose robotics for mAniPulation of defoRmable materIaLs in manufacturing processes

Periodic Reporting for period 1 - APRIL (multipurpose robotics for mAniPulation of defoRmable materIaLs in manufacturing processes)

Reporting period: 2020-04-01 to 2021-05-31

APRIL is a 40 months project that started on April 1st 2020, co-funded by the European Union’s Horizon 2020 research (grant agreement No 870142) under the domain of the Factories of the Future. The APRIL Project driven by 15 partners from 8 European countries.

APRIL aims at providing a technological infrastructure and interoperable methods, tools, and services that will support multipurpose, and easy to repurpose, autonomous dexterous robots able to manipulate, assemble and process different soft, deformable and flexible materials in a production line environment; making possible industrial innovation, cobotics, growth of business value and capabilities in the manufacturing sector, especially in manufacturing SMEs. The following seven specific objectives support the overall strategic goal:

• To provide a scalable and beyond state of the art modular robot prototype with high dexterity for manipulation of flexible materials by Y3 of the project. This prototype will be scalable in functions and connected as a plug-in to an existing knowledge base in the cloud, achieving high-level reasoning capabilities.
• To improve robot Grasping and Manipulation (G&M) to move towards multipurposed approach, by acquiring different skills to manipulate at least 3 types of flexible materials (food, plastics, papers, etc.) and 5 different characteristics (texture, size, shape, weight, colour, material composition, etc.) of the flexible materials.
• To develop a Knowledge Reasoning Engine Module (KREM) to enable grasp planning in complex scenes.
• To foster a ground-breaking standardized perceptual system.
• To design a proactive safety preservation and ergonomic optimization approach.
• To test and validate APRIL prototype under the federated machine learning through six use case demonstrators in five countries.
• To underpin Business Model Innovation (BMI) for robotics, creating new paths for sustainability of actions.

In APRIL, the union of fine grasping provided by APRIL robots, data from sensors and computational vision technology; coupled with a set of modular and different middleware layers and interfaces, provide the perceptual and contextual information that allows robots to sense and understand the production environment. This, allows to successfully manipulate a wide range of soft objects, learn, plan and execute ergonomic motions, making human robot collaboration simpler and more efficient. A federated approach, connects robots to a cloud based knowledge base that will contain the needed information to support robots performing the different jobs.
The course of action within APRIL project is organized in three overlapping and agile cycles that comprises the development of all project processes towards accomplishing project objectives; which starts with a foundational phase that gathers all requirements and continuous with two iterative phases that implements incremental versions of the robotic based solutions (i.e. version α and β). The APRIL robotic based solutions will be validated and tested with 6 high impact-oriented demonstration use cases manipulating deformable objects of different types (e.g. paper, chicken breast, shoes’ insoles, viscoelastic textile materials, cables, etc.) and showing the real-value of the produced outputs.
During this first year, the project efforts were focused on consolidating the first and part of the second cycles of the project. On one hand, actions centred in securing the project roll-out and consolidating a shared vision for common understanding among partners on the operational details of the project. On the other hand, efforts brought to bear the ground for the gathering of requirements and designing APRIL infrastructures and services. Main activities in this regard, comprised:
• Close integration and interaction between of the different work areas.
• Analysis and definition of the APRIL requirements, architecture, success criteria, implementation, and testing/validation processes. Achievements in this area comprise:
o Elicitation of use cases technical and functional requirements, and generation of information flow models reflecting the requirements analysis, modelling of information and knowledge for integrating the robots and enabling human-robot interaction in the domains of the APRIL scenarios/use cases,
o Definition of requirements for robotics system and relative architecture,
o Development of initial studies on collaborative problem solving, collaborative design and decision-making processes,
o Simulation of the knowledge to be represented.
• Based on the requirements, we have set into motion the refinement and implementation of the APRIL robotic infrastructure, starting with testing the grasping and robot movements in laboratory environments, including experiments for advanced grasping and human-robot interaction; to further deliver the APRIL core services to use cases demonstrators at the end of the 2021 year.
The importance of activities and tasks developed in APRIL lie on the contribution to the advances and adoption of dextrous hand robots to the manipulative operation of deformable and/or flexible objects in production lines, which can be found in many industrial domains, such as automobiles, textiles, electronic components or packaging. Most of the manipulation tasks involving the handling of deformable objects are done manually, which makes them labour intensive and time consuming. APRIL results will impact important manipulative operations dealing with deformable objects, such as whole body product manipulation, shape changing or biomanipulation (e.g. food). Thus, APRIL also aims at impacting society by effectively supporting collaborative approaches that overcome these labour intensive, repetitive and/or physically demanding work, that could cause repetitive-strain injuries; while at the same time integrating shop floor workers in shaping of digital solutions and managerial actions, empowering those workers.
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