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Machines 2017, 5(1), 3; doi:10.3390/machines5010003

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
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)
View Full-Text   |   Download PDF [30440 KB, uploaded 18 January 2017]   |  

Abstract

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. View Full-Text
Keywords: robotic milling; robot stiffness modeling; optimal motion planning; robotic manufacturing robotic milling; robot stiffness modeling; optimal motion planning; robotic manufacturing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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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