Speeding Up The Learning of Robot Kinematics Through Function Decomposition  

Oleh:

Torras, C. ; Angulo, V. R. de

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 6 (Nov. 2005), page 1504-1512.
Topik: kinematics fundamentals; robot kinematics; function decomposition

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The main drawback of using neural networks or other example - based learning procedures to approximate the inverse kinematics (IK) of robot arms is the high number of training samples (i. e., robot movements) required to attain an acceptable precision. We propose here a trick, valid for most industrial robots, that greatly reduces the number of movements needed to learn or relearn the IK to a given accuracy. This trick consists in expressing the IK as a composition of learnable functions, each having half the dimensionality of the original mapping. Off - line and on - line training schemes to learn these component functions are also proposed. Experimental results obtained by using nearest neighbors and parameterized self - organizing map, with and without the decomposition, show that the time savings granted by the proposed scheme grow polynomially with the precision required.

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