A Novel Trajectory Generation Method for Robot Control

被引:0
|
作者
KeJun Ning
Tomas Kulvicius
Minija Tamosiunaite
Florentin Wörgötter
机构
[1] University of Göttingen,Bernstein Center for Computational Neuroscience, Inst. of Physics III
[2] Research & Technology,undefined
[3] Lenovo,undefined
来源
Journal of Intelligent & Robotic Systems | 2012年 / 68卷
关键词
Trajectory generation; Dynamic trajectory joining; Control theory; Machine learning;
D O I
暂无
中图分类号
学科分类号
摘要
This paper presents a novel trajectory generator based on Dynamic Movement Primitives (DMP). The key ideas from the original DMP formalism are extracted, reformulated and extended from a control theoretical viewpoint. This method can generate smooth trajectories, satisfy position- and velocity boundary conditions at start- and endpoint with high precision, and follow accurately geometrical paths as desired. Paths can be complex and processed as a whole, and smooth transitions can be generated automatically. Performance is analyzed for several cases and a comparison with a spline-based trajectory generation method is provided. Results are comparable and, thus, this novel trajectory generating technology appears to be a viable alternative to the existing solutions not only for service robotics but possibly also in industry.
引用
收藏
页码:165 / 184
页数:19
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