Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/46189
Type of publication: research article
Type of publication (PDB): Straipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)
Field of Science: Informatika / Informatics (N009)
Author(s): Kulvičius, Tomas;Ning, KeJun;Tamošiūnaitė, Minija;Wörgötter, Florentin
Title: Joining movement sequences : modified dynamic movement primitives for robotics applications exemplified on handwriting
Is part of: IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society. New York : IEEE Press, Vol. 28, no. 1, 2012
Extent: p. 145-157
Date: 2012
Keywords: Delta learning rule;Handwriting generation;Joining of dynamic movement primitives (DMPs);Overlapping kernels
Abstract: The generation of complex movement patterns, in particular, in cases where one needs to smoothly and accurately join trajectories in a dynamic way, is an important problem in robotics. This paper presents a novel joining method that is based on the modification of the original dynamic movement primitive formulation. The new method can reproduce the target trajectory with high accuracy regarding both the position and the velocity profile and produces smooth and natural transitions in position space, as well as in velocity space. The properties of the method are demonstrated by its application to simulated handwriting genera-tion, which are also shown on a robot, where an adaptive algorithm is used to learn trajectories from human demonstration. These re-sults demonstrate that the new method is a feasible alternative for joining of movement sequences, which has a high potential for all robotics applications where trajectory joining is required
Internet: https://hdl.handle.net/20.500.12259/46189
Affiliation(s): Sistemų analizės katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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