Research project

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

Horse aims to develop technologies for Smart Factories, in which robots and humans collaborate, more flexible, more efficient and more effective.

Duration
September 2015 - July 2020

HORSE

This is a research and development project in the European Horizon 2020 framework. It aims to develop technologies for Smart Factories, making end-to-end high tech manufacturing processes, in which robots and humans collaborate, more flexible, more efficient and more effective to produce small batches of customized products. This is done through the use of Internet of Things, Industry 4.0, collaborative robot technology, dynamic manufacturing process management, and flexible task allocation between robots and humans. The innovative view behind HORSE builds on two main developments:
• the seamless integration of human and robotics actors in manufacturing steps, including advanced situation awareness to guarantee safety and augmented reality for task support;
• the integration of horizontal manufacturing processes across individual manufacturing steps (and linked to end-to-end business processes) with vertical manufacturing activities within individual steps.

As part of these developments, the TU/e team focuses on the application, and extension of existing Business Process Management (BPM) technologies for the high tech manufacturing domain, called Manufacturing Process Management (MPM). Benefits that the MPM system can bring are in the coordination and monitoring of the horizontal process on different levels. By standardizing and orchestrating the execution of the process the system provides an up-to-date overview of the end-to-end manufacturing process in terms of e.g. machine status, availability of resources, and traceability of (intermediate) products. The system coordinates tasks and dynamically assigns the most suitable resources (i.e. humans, robots, AGV, etc. in the factory) to execute the tasks. It also supports structured exception handling when e.g. defects, or alarms occur.

Researchers involved in this project

Collaborative Partners

  • Eindhoven University of Technology
  • European Dynamics S.A.
  • Technische Universität München
  • Bosch
  • KUKA
  • FZI
  • CEA
  • CETIM
  • HUA
  • Thomas Regout International
  • Sercobe
  • TCS
  • Odlewnie Polski
  • Netherlands Organisation for Applied Scientific Research

Subsidy Provider