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Smart integrated Robotics system for SMEs controlled by Internet of Things based on dynamic manufacturing processes

Periodic Reporting for period 3 - HORSE (Smart integrated Robotics system for SMEs controlled by Internet of Things based on dynamic manufacturing processes)

Reporting period: 2018-11-01 to 2020-07-31

Think of a metal factory worker manipulating and finishing a heavy sand cast part full of sharp edges with a pair of gloves, a hammer and a heavy grinder as only tools - and imagine how a dynamically available robotic handling arm can improve this situation. Think of a car factory where human workers and robots are strictly separated - and imagine how safe collaboration of both can make production much more efficient. Think of a robotic production line where a sudden robot failure brings things to a grinding halt - and imagine how safe human take-over of its task can bring things up-to-speed swiftly. You are now imagining what HORSE will bring: robotics assistance to improve operators conditions of work, improve their safety and at the same time provide a way to gain in production effectiveness and efficiency.

HORSE brought a leap forward in manufacturing proposing a new flexible model of smart factory involving collaboration of humans, robots, AGVs and machinery to realize industrial tasks in an efficient manner. HORSE fosters technology deployment towards SMEs by developing a methodological and technical framework for easy adaptation of robotic solutions and by setting up infrastructures and environments acting as European and Regional hubs. HORSE Framework, depicted in a reference architecture and implementation, facilitates the customization, use and reprogramming of robots. Being scalable, it is possible to start the digitization with one part of the process (even just one workcell) and then expand to the whole production.
The framework is driven and validated first in 3 pilots demonstrating challenges : (a) robotics co-manipulation, (b) hybrid position/force control co-working, (c) flexible assembly and maintenance. At second, 7 experiments are recruited by an Open Call, even extending functionalities.
HORSE pioneered in several fronts and raised the ambition level in smart manufacturing projects:
• Its Reference architecture for a cyber-physical system orchestrates production agents and digitize the manufacturing process. Positioned against industry standards.
• Implemented this architecture with the partners tools and adopted it in 10 manufacturing industries to address unresolved challenges.
• Placed over existing technologies and tools in a software stack that proposes alternative implementations to fit various needs.
• 3 pilots in REAL factory conditions (highly ambitious), deployed within factories. Beyond standard lab testing.
• 5 DiHs in 5 regions (new approach) were connected and benefited by the project DIH establishment approach and proved increased business, research and capabilities.
• Established 5 CC in other 5 regions (4+1) moving beyond its contractual obligations. Developed novel business models and demonstration scenarios for the HORSE technologies and promoted new business.
• 7 Application Experiments in new industrial applications executed in parallel and provided new demonstration scenarios and new valuable tools in the HORSE Framework.
• Business modeling for placing the HORSE framework considering the necessary co-creation.
• Pioneering with transforming European robotic ecosystem and equipping them with technologies and knowledge. Other projects on creating DIH networks were based on this result.
• Devised and applied a Cost-Benefit methodology for the future SMEs that consider the alignment with Industry 4.0.
• The HORSE reference architecture, updated to accommodate the advances in the modules and the feedback from its adopters.
• Integration resulted in TRL7 applications, already beyond standard lab demonstrators. 2 additional pilot cases implemented in factories (TRI, OPSA).
• 7 additional experiments demonstrated solutions for real industrial challenges and validated components and technologies.
• 5 CCs established (4 foreseen in DOA). CCs fully adopted and demonstrate HORSE framework and its value in tailored scenarios that complement their specialisaiton. Sustainability plans for broader developments aligned with European focus on Digital Innovation Hubs (DIHs).
• Dedicated workshops focused on defining the exploitation strategy based on the BASE/X methodology for HORSE, its market positioning, and the role of the CCs in their regions and other dedicated workshops supported the future business of the 7 experiments that were selected with an open call procedures.
• Cost/Benefit methodology was defined and adopted.
• Communication and awareness raising activities:
o The dissemination material and online presence are continuously updated.
o 10+ press articles.
o 6 peer-reviewed publications.
o 35+ presentations/posters in events.
o HORSE workshops with SMEs.
o HORSE wiki and book
HORSE components and technologies innovate and address real industrial challenges, and at the same time validate novel approaches for digitizating and automatizing manufacturing:
• Robotic agents enhanced with practical aspects to allow easy integration in working environments, controlled by MPMS.
• MPMS orchestrates the control of both human and robotic agents. It also supports production planning and minimizes changeovers.
• Middleware adopts existing protocols and standards, such as ROS, OSGi, OPC UA and connect cyber and physical layers.
• Robotic agents used in real industrial tasks and enhanced with global safety features, and better monitoring and control.
• Adaptive collision detection detects obstacles in the workspace, in the robot trajectory.
• Hybrid Task Supervisor, at the local task execution of both human and robots agents, receives task requests and keeps track of the execution progress, through a user-friendly GUI.
• The industrial robots have been enhanced with safety features previously found only on less demanding collaborative robots.
• Enhanced situation awareness: at the level of robot environment-related data, robot position and information about its planned trajectory are considered to reselect the trajectory in case of potential collision. At the level of workcell identifies ‘out-of-normal’ situations. A the level of production line relies on environment data representation and analysis, triggering decisions to agents based on reasoning.
• A position-force control system eases the robot task programming and allows safe human-robot interaction, with better performance in trajectory tracking and force control.
• Safety standards have been reviewed in terms of the above mentioned contributions
• The Augmented Reality system supports intuitive task instructions, enabling non-expert workers to perform tasks, previously requiring extensive experience.
• The Learning by Demonstration functionalities allow the robot to be programmed easier and faster than with traditional programming.
• Task instructions and alerts are now delivered more comprehensively and effectively in electronic format.

The project paved new way of I4MS objectives to promote robotics in the European SMEs:
• Demonstrations target existing and unresolved challenges of the factories and are motivating for other SMEs to adopt them. HORSE solutions are replicable in other factories, as confirmed by the feasibility studies of DIHs.
• CCs in 5 regions have been equipped with competences, demonstration scenarios, new business approach promoting HORSE framework.
• 7 experiments mentored by HORSE adopted the FWK and demonstrated valuable case studies.
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