NDLI: Navigation functions learning from experiments: Application to anthropomorphic grasping

Content Provider	IEEE Xplore Digital Library
Author	Filippidis, I.F. Kyriakopoulos, K.J. Artemiadis, P.K.
Copyright Year	2012
Description	Author affiliation: Human-Oriented Robotics and Control Lab, School for Engineering of Matter, Transport and Energy, Ira A. Fulton Schools of Engineering, Arizona State University, USA (Artemiadis, P.K.) \|\| Control Systems Lab, Department of Mechanical Engineering, National Technical University of Athens, 9 Heroon Polytechniou Street, Zografou 15780, Greece (Filippidis, I.F.; Kyriakopoulos, K.J.)
Abstract	This paper proposes a method to construct Navigation Functions (NF) from experimental trajectories in an unknown environment. We want to approximate an unknown obstacle function and then use it within an NF. When navigating the same destinations with the experiments, this NF should produce the same trajectories as the experiments. This requirement is equivalent to a partial differential equation (PDE). Solving the PDE yields the unknown obstacle function, expressed with spline basis functions. We apply this new method to anthropomorphic grasping, producing automatic trajectories similar to the observed ones. The grasping experiments were performed for a set of different objects, Principal Component Analysis (PCA) allows reduction of the configuration space dimension, where the learning NF method is then applied.
Starting Page	570
Ending Page	575
File Size	1935447
Page Count	6
File Format	PDF
ISBN	9781467314039
ISSN	10504729
e-ISBN	9781467314053
e-ISBN	9781467314046
DOI	10.1109/ICRA.2012.6225168
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise measurement Trajectory Splines (mathematics) Robots Grasping Principal component analysis Humans
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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An anthropomorphic navigation scheme for dynamic scenarios

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