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

Periodic Reporting for period 2 - HORSE (Smart integrated Robotics system for SMEs controlled by Internet of Things based on dynamic manufacturing processes)

Reporting period: 2017-05-01 to 2018-10-31

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 brings 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. The HORSE Framework, depicted in a reference architecture and implementation, facilitates the customization, use and reprogramming of robots, limiting the need for high-qualified personnel. 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 suitability of the framework is driven and validated in two steps:
At first, it is installed in 3 pilots demonstrating challenges solved by HORSE: (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 the HORSE functionalities.

The HORSE technological framework is installed in 5 Competence Centres (CCs) which define, implement and assess the framework, capitalizing lessons learned and best practices.
• The HORSE reference architecture, updated periodically
• The following components have been completed and demonstration videos can be found on the website:
o Manufacturing Process Management System (MPMS), one of the main components of the HORSE framework
o Augmented Reality software for user assistance, already integrated within a pilot since Oct ‘18
o Multimodal monitoring system, in lab
o Intuitive programming for robots and online, dynamic motion planning, in lab
o HORSE middleware, connecting layer 3 (MPMS) with the bottom layer (agents)
o Force-control and situation awareness based on signal deviation analysis
• Ongoing integration is demonstrated in TRL7, already beyond many other project demonstrators. Two pilots have been installed (BOS, TRI), and one was demonstrated in lab while the deployment plan has been set up (OPSA).
• All components were validated by end-users. Feedback received was overall positive and the factories are even considering further investments.
• The CCs have been established in business terms. CCs will fully adopt and demonstrate the HORSE framework and its value. Sustainability plans have been put into action in the context of the broader developments and European focus on the concept of Digital Innovation Hubs (DIHs).
• HORSE mentored 5 DIHs, selected by the I4MS open calls, to adopt the framework and propose regional use cases. In many cases they have been further developed and reported remarkable progress in becoming regional robotic centres.
• Two 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.
• Communication and awareness raising activities have been promoting the project and its results in web, social and physical means:
o The dissemination material and online presence are continuously updated.
o 10+ press articles.
o 4 peer-reviewed publications; more expected.
o 35+ presentations/posters in events.
o HORSE workshops with SMEs.
HORSE contributes to important directions in terms of State-of-the-art by providing components and technologies which innovate and address real industrial challenges, and at the same time prove novel approaches for digitizating and automatizing manufacturing:
• The robotic agents have been enhanced with practical aspects to allow them to easily integrate in working environments, controlled by MPMS, which orchestrates the control of both human and robotic agents. It also supports production planning and minimizes changeovers.
• The communication adopts existing protocols and standards, such as ROS, OSGi, OPC UA.
• Robotic agents have been used in real industrial tasks and enhanced with global safety features, and better monitoring and control.
• Adaptive collision detection detects obstacles in the workspace, specifically only in that volume that will be passed through by the robot in the near future. It uses the knowledge of the planned robot trajectories, monitoring the required volume for collisions.
• The Hybrid Task Supervisor, related to the local execution of a task by both the human workers and the robots, 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 different levels. The first level (robot task) takes into account environment-related data, robot position and information about its planned trajectory to reselect the trajectory in case of potential collision. The second level (workcell sensor analysis) identifies ‘out-of-normal’ situations. The third level (production line) relies on environment data representation and analysis, triggering decisions to the 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 contributes also to the achievement of the I4MS objectives to promote robotics in the European SMEs:
• The pilot demonstrations target existing and unresolved challenges of the factories and can be motivating for other SMEs to adopt them. The solutions provided in HORSE can be replicated in other factories, as confirmed by the feasibility studies of the DIHs.
• CCs in 5 regions have been equipped with competences and demonstration scenarios promoting the HORSE framework, with the opportunity to further develop their business potential.
• The HORSE DIHs which have been mentored by HORSE have shown further progress and paved the way for the European industrial transformations in areas previously not fully involved in the robotics.
The HORSE Logo