Dr. Michael Suppa, Roboception GmbH, Germany
Prof. Markus Vincze, TU Vienna, Austria
Christian Baumgartner TGW Logistics Group, Austria
Dr. Patrick Courtney, Tec-connection, UK
Christos Gkournelos , LMS, University of Patras, Greece
Motivation and Objectives:
Perception is one of the key technologies for enabling flexible production processes, such as pick and place, machine tending, assembly, and quality testing. Applied AI allows to solve complex automation problems as data and model-driven machine learning approaches reduce manual parameterization effort significantly.
The combination of machine learning and classical methods provide reliability, robustness and flexibility to fulfill the requirements in lab automation, agile production, and logistics. These solutions increase the flexibility making the return-on-invest much easier to demonstrate, especially for SMEs.
Industrial use cases are elaborated in an interactive session with the attendees. The attendees are guided by key statements. The goal is to create synergies and potential collaborations to facilitate the introduction of AI-driven perception into novel robotic applications.
16:10 Introduction and Definition of Statements/ Key Questions, Dr. Michael Suppa, Roboception GmbH
16:20 Towards Detecting and Grasping Transparent Objects, Prof. Markus Vincze, TU Vienna, Austria
16:30 AI Driven Vision in Logistics, Christian Baumgartner TGW Logistics Group, Austria
16:40 Perception Challenges and Requirements in Lab Automation, Dr. Patrick Courtney, Tec-connection, UK
16:50 Model-based Machine Learning for Pick-and-Place in Agile Production, Dr. Michael Suppa, Roboception GmbH, Germany
17:00 Cooperating Robots and AppliedAI for Reconfigurable Manufacturing, Christos Gkournelos , LMS, University of Patras, Greece
17:10 Interactive Session/ Round Table Discussion
17:25 Conclusion and take home messages