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Content Provider | IEEE Xplore Digital Library |
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Author | Ulbrich, S. Bechtel, M. Asfour, T. Dillmann, R. |
Copyright Year | 2012 |
Description | Author affiliation: Humanoids and Intelligence Systems Lab, Institute for Anthropomatics at the Karlsruhe Institute for Technology, Germany (Ulbrich, S.; Bechtel, M.; Asfour, T.; Dillmann, R.) |
Abstract | The previously presented Kinematic Bézier Maps (KBM) are a machine learning algorithm that has been tailored to efficiently learn the kinematics of redundant robots. This algorithm relies upon a representation based on projective geometry that uses a special set of polynomial functions borrowed from the field of Computer Aided Geometric Design (CAGD). So far, it has only been possible to learn a model of the forward kinematics function. In this paper, we show how the KBM algorithm can be modified to learn the robot's equation of motion and, hence, its inverse dynamic model. Results from experiments with a simulated serial robot manipulator are presented that clearly show the advantages of our approach compared to general function approximation methods. |
Starting Page | 3598 |
Ending Page | 3604 |
File Size | 1503586 |
Page Count | 7 |
File Format | |
ISBN | 9781467317375 |
ISSN | 21530858 |
e-ISBN | 9781467317368 |
DOI | 10.1109/IROS.2012.6386057 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-10-07 |
Publisher Place | Portugal |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Mathematical model Joints Equations Robot kinematics Kinematics Heuristic algorithms |
Content Type | Text |
Resource Type | Article |
Subject | Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software |
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