Automatic Motion Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning
1
Fraunhofer Institute for Manufacturing Engineering and Automation (IPA), Nobelstrasse 12, D-70569 Stuttgart, Germany
2
Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW), University of Stuttgart, Seidenstrasse 36, 70174 Stuttgart, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Dan Zhang
Machines 2017, 5(1), 3; https://doi.org/10.3390/machines5010003
Received: 1 November 2016 / Revised: 20 December 2016 / Accepted: 9 January 2017 / Published: 18 January 2017
(This article belongs to the Special Issue Robotic Machine Tools)
Optimal and intuitive robotic machining is still a challenge. One of the main reasons for this is the lack of robot stiffness, which is also dependent on the robot positioning in the Cartesian space. To make up for this deficiency and with the aim of increasing robot machining accuracy, this contribution describes a solution approach for optimizing the stiffness over a desired milling path using the free degree of freedom of the machining process. The optimal motion is computed based on the semantic and mathematical interpretation of the manufacturing process modeled on its components: product, process and resource; and by configuring automatically a sample-based motion problem and the transition-based rapid-random tree algorithm for computing an optimal motion. The approach is simulated on a CAM software for a machining path revealing its functionality and outlining future potentials for the optimal motion generation for robotic machining processes.
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MDPI and ACS Style
Diaz Posada, J.R.; Schneider, U.; Sridhar, A.; Verl, A. Automatic Motion Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning. Machines 2017, 5, 3.
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