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Automatic optimal motion generation for robotic manufacturing processes: Optimal collision avoidance in robotic welding

: Diaz Posada, Julian Ricardo; Dietz, Thomas; Ockert, Philip; Kuss, Alexander; Hägele, Martin; Verl, Alexander

Postprint urn:nbn:de:0011-n-4150447 (1.5 MByte PDF)
MD5 Fingerprint: fd73b339b98cb2332d570a8cb7bc5378
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Created on: 29.9.2016

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Robotics and Automation Society:
IEEE International Conference on Automation Science and Engineering, CASE 2016 : 21-25 August 2016, Fort Worth, Texas, USA
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-2410-0 (Print)
ISBN: 978-1-5090-2409-4
ISBN: 978-1-5090-2408-7
Conference on Automation Science and Engineering (CASE) <12, 2016, Fort Worth/Tex.>
European Commission EC
The European Robotics Initiative for Strengthening the Competitiveness of SMEs in Manufacturing by integrating aspects of cognitive systems
European Commission EC
H2020; 688217; ROBOTT-NET
ROBOTT-NET - A shared infrastructure to sustainably optimise technology transfer throughout Europe
Conference Paper, Electronic Publication
Fraunhofer IPA ()
Roboterprogrammierung; Small and Medium Sized Enterprises (SME); Kleine und mittlere Unternehmen KMU; manufacturing; Bewegungsablauf; Schweißroboter

Optimal, efficient and intuitive robotic programming is still a challenge in robotic manufacturing and one of the main reasons why robots are not widely implemented in small and medium-sized enterprises (SME). In order to effectively and efficiently respond to the current product variability requirements, SMEs require easy and optimal programmable robotic manufacturing systems in order to achieve profitable and rapid changeover. To make up for this deficiency, this paper proposes a solution approach for computing optimal motions for manufacturing processes based on the interpretation of the manufacturing process and an automatic configuration of a state of the art sample-based algorithm, the Rapidly-exploring Random Tree RRT* which is provably asymptotically optimal, using as inputs the semantic and mathematical descriptions of the product, process and resource components. The approach is simulated on the example of collision avoidance for different scenarios in robotic welding revealing its functionality and outlining future potentials for the optimal motion generation for robotic manufacturing processes.