RAMPup builds upon five of Key Enabling Technologies (KETs) for Flexible Automation

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odular Automation concerns the proficiency of defining standardized interfaces for robotic automation components to enable easier re-configurability, reusability and interchangeability. Modularity can be applied on many levels, and has already been extensively applied to automation components such as PLCs, Grippers and tools, robot configurations, and construction profile systems. RAMPup defines interfaces on a higher level for self-contained Robotic Automation Modules encapsulating complete process or sub-process functionality including hardware, software and control. Modularity enables the distribution of functionality and control into smaller entities. When setting up a large system these entities can be considered as black boxes of functionality. Thus the integration of a larger system is simplified, and the integration of the module itself is delimited by its own functionality. Modularity also increases the robustness, as failing modules can be rapidly replaced without disturbing the whole system. New modules can also be developed decoupled from the larger system, and their functionality added later with limited downtime. Modularity enables scalability. Smaller solutions can be applied and evaluated before scaling up. In Human-Robot Co-operation the level of automation can be applied in smaller steps, increasing the level over time if there is a need to ramp up. This also result in smaller investment and organizational changes initially, and thus it is associated with less risk.
Modular Automation

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kill-based Execution applies advanced robotic control strategies by simple semantic actions. The user can invoke complex control strategies encapsulated as skills, such that e.g. riveting a product is achieved by merely inputting the position of the hole. The complex control strategies needed for a Robotic Automation Module to actually apply the rivet are hidden behind a skill. Superusers and process experts such as module developers and system integrators can develop the complex control strategies of the skills. RAMPup provides the framework enabling the skills to accompany the modules. Being able to perform overall system control using definitions at a “skill” level ties in closely to the integration of intuitive user-interfaces. The semantic descriptions are easier for a user to manage. Skill-based execution is very much related to modularity in the context of dividing functionalities into more manageable entities. The combination of the two through Robotic Automation Modules brings robotic automation to an even higher level.
Skill-based Execution

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uman-Robot Co-operative Workstations increases the productivity of a workstation by finding a good balance between manual processes and automated processes while keeping the business-case positive. In many cases a sub-process which is simple for a human worker to complete can be very difficult to automate. This sub-process may require a majority of the resources spent on automating the complete process, and thus the business-case fails. RAMPUP enables setting up co-operative workstations and the opportunity to change the balance between manual and automated processes for ramping up and down the production. An additional benefit is minimizing worker repetitive strain injuries by applying robotic automation to repetitive work. Unlike human-robot collaborative workstations focusing on direct interaction between the operator and the robotics components, co-operative workstations focus on balancing processes between the operator and the robotic components to complete the process. Safety is not in focus in RAMPup, but neither is it negligible. In the integration of the Robotic Automation Modules the aim is to allow fenceless operation. Despite not requiring direct interaction, the workflow and cost may benefit from fenceless operation. Alternatively, it can be ensured that the module is inoperable in close proximity to the operator. This is taken to account by the system integrators in the specific individual modules and use cases, and risk assessments for each are provided.
Human-Robot Co-operative Workstations

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dvanced Sensor Integration makes it possible for the system to adapt the automated process in real time, in order to adapt to any changes in the environment or construction. Traditional automation solutions are usually highly dependent on tolerances in their construction, and extremely vulnerable to even small offsets from the initial calibration. With the introduction of advanced sensors in RAMPUP , the workstations become significantly more robust to such variations. In addition to improving general robustness, introduction of advanced sensors greatly lowers the need for the construction and integration of specialized fixtures in new automation solutions, as clever use of sensor data can make even traditional hardware adaptable to a wide range of applications. With advanced machine vision for example it is possible to directly reuse major sub-processes from one implementation in another, as modifying the vision algorithm can repurpose an entire setup to handle other parts or processes. In a co-operative workstation it is crucial to have this capability, as the uncertain nature of human operations (e.g. placing) will require significant adaption of the robot behaviour during runtime.
Advanced Sensor Integration

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asier Instruction through Intuitive User Interfaces enables operators to automate SME-like productions by being able to easily instruct new processes or partly reinstruct or modify existing processes. Both greatly reducing automation maintenance and adaption cost over time, and actively involving employees through direct interaction with the automation cell, an intuitive user interface enables utilization of the full potential of the workstation. The approach used in RAMPup to reach an intuitive and useful user interface, is to focus on end user needs in real industrial production environments. Through studies of interaction needs by operators in relevant use cases, RAMPup aims to integrate an optimal user interface. This is done by exploiting existing core interface components and focusing on the integration and adaption to support real needs of the manufacturing industry.
Easier Instruction

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