<< Objectives >>The Objective of this project is to create an educational system for mechatronics students that gives the students an opportunity to learn using real mechatronic hardware. Many students have specific learning needs, but want to learn and also want to be included in the educational process like everyone else. That is why we propose that all students get their own educational hardware with online support (MOOCs ) to be able to adjust practical mechatronics study to their needs.<< Implementation >>Activities of this project will be mechatronic educational hardware development, creation of supporting mechatronics courses and tutorials in the form of MOOCs, development of platforms for the hardware re-use or sharing and project results promotion. Finally 4 mechatronics summer schools for university students will be implemented to evaluate the developed hardware and MOOCs.All work packages will have an activity sequence of specification definition, development, evaluation and modification.<< Results >>Results of the project will be:- Educational hardware in the form of smart servo motor will provide students with a device that equally represents all three study fields of mechatronics.- Online mechatronic courses and tutorials in the form of MOOCs will support students’ learning with the hardware at university and at home.The project results will be shown to be ready for ecologically responsible use in the university mechatronics education process to equalise the mechatronics students.

Many industries are transitioning to I4.0 production models by adopting robots in their processes. In parallel, XR technologies have reached sufficient maturity to also enter the domain of industrial applications, with early success cases often related to the training of workers, remote assistance, access to contextual information and interaction with digital twins. This project looks at the intersection of both technologies: robots and XR. The use of robots in industry will be increasingly enhanced with XR applications, and workers must be able to understand both technologies and use hybrid solutions confidently. Achieving this is a challenge that education and training programs must meet. The objective of MASTER is to boost the XR ecosystem for teaching and training of robotics in manufacturing by providing an Open XR platform that integrates key functionalities for creating safe robotic environments, programming flexible robotic applications (programming by demonstration in flexible robotic application development) and integrating advanced interaction mechanisms (innovation in gaze-based interaction). MASTER will also deliver rich training content on robotics. MASTER proposes integrating third party contributions through two Open Calls: The first one aims to provide the platform with additional technologies and functionalities. The selected companies will have the opportunity to integrate their technology in the platform and test it with a wide range of end-users. The second Open Call is aimed at the education sector, by offering the possibility to test first-hand the platform and tools to create their own content.

During the last years, EU manufacturing has faced production flexibility challenges by deploying, among others, novel hybrid manufacturing systems, involving collaborative robots and mobile manipulators combined with flexible grippers, vision systems, sophisticated tasks/actions planning solutions and flexible integration platforms. Despite the importance of AI enabled flexible robotic systems, several aspects settle back their wider adoption, and impact on the objectives of the green deal: •Limited cognition/ intelligence: existing solutions support non-trivial tasks but cannot act autonomously. •Insufficient perception and diagnostics: In a circular economy, there is an increased need for understanding the state of products or parts that are being handled, after they have been used. •Decision making is restricted: Current decision-making focuses on process or line level, not taking into account optimization at value chain level or per individual product. •Small scale adaptation of AI due to small number of available data and training needed, to support tailored solutions in high variability context. •Lack of use of explicitized knowledge in AI and robotics. Lifecycle data and knowledge is not used across the value chain to improve decision making after a product’s first life. •Complexity in robot programming and interaction which requires the involvement of skilled engineers, does not provide flexibility in execution, Thus, ROB4GREEN aims to develop easy to use and deploy AI driven collaborative robotic systems, that can reason and adapt to a variety of strategies for processing products after their first life, both hardware and behavior wise, improving existing skills and generating new ones, working autonomously combining data and knowledge. Such systems will be validated at scale and in major industries, showcasing optimization ranging from cell to the whole value chain, towards achieving significant impact on the objectives of the green deal.

Over the last years, production has been shifted from mass production to customization. The conventional production lines, traditionally focused on one product variant or one family of products do show their limitations to cope with the new needs. Moreover, unprecedented worldwide events, such as the recent pandemic crisis, indicated even more the need for flexible production systems that can rapidly switch production to a totally different one (e.g. automotive manufactures had to produce respirators, facemasks etc.). As a response, MASTERLY aims to develop flexible robotic solutions, constituting of modular grippers combined with state-of-the-art robotic technologies, such as mobile, high and low payload industrial and collaborative robots and smart cranes, enhanced with AI driven advanced control and perception capabilities that will allow them to act autonomously, handling a large variety of parts varying in size, shape and material, while being acceptable by both genders of workforce. The developments will focus around the following 5 pillars: 1) Innovative, efficient and low consumption systems for storage, retrieval, conveying and pick-and-place using a multi-disciplinary approach combining technologies 2) Robust handling devices and systems, with integrated –AI driven- advanced control 3) User-friendly interfaces for robot/machine control and programming 4) Interoperable S/W and H/W interfaces 5) Industrial Pilot Cases for work piece handling in full production line The technologies will be tested for flexibility, efficiency & user acceptance in three use cases from different productions sectors, aiming to demonstrate production line and cross sector applicability and adaptability: Elevators manufacturing, focusing on the assembly of electrical cabinets of lifts (KLEEMANN), Sportswear, focusing on warehouse logistics and packaging (DECATHLON) and Aeronautics production, focusing on production of large composite panels of aircraft wings (AERNNOVA).

To strengthen the competitiveness of the EU industry, there is an increasing need for flexible production, exploiting the capabilities of both the machinery and human workforce. The growing market of robots has reacted, adapting to the changing situation by providing collaborative robotic solutions with a variety of characteristics in terms of payload, type and mobility. A number of technological solutions for perception and safety allow their coexistence with humans. While several research activities have been conducted over the past 5 years on this topic, wider industrial adoption is lagging due to the following restrictions: • Limited cognition and intelligence • Low performance of collaborative operations • Collaboration fluidity is rather low as the operators have to adapt to the particularities of the robots. • Complexity in robot programming which requires the involvement of skilled engineers, does not provide flexibility in the execution phase, and does not benefit from the tacit knowledge of experienced operators. Thus, despite the fact the mechatronics are quite advanced, a perfect matching of mechanics and control is required to create robots that advance from repetitive and precision-oriented tasks to becoming intelligent and helpful co-workers. Proper human robot interaction is the most important ground to be covered. Thus, JARVIS aims to develop a reusable set of tools that enable AI driven multimodal means of interaction: a) involving interfaces for physical and remote information exchange, robot control and programming, b) providing social skills to a variety of robots to achieve seamless user-centric interaction that extends human ability for complex tasks and c) demonstrating scalability of application and ability to achieve economies at scale.