COLLISION DETECTION FOR MULTIPLE ROBOT MANIPULATORS BY USING ORTHOGONAL NEURAL NETWORKS

NCUIR > Engineering College > Graduate Institute of Mechanical Engineering > journal & Dissertation > Item 987654321/27060

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Title:	COLLISION DETECTION FOR MULTIPLE ROBOT MANIPULATORS BY USING ORTHOGONAL NEURAL NETWORKS
Authors:	TSENG,CS;WU,CC
Contributors:	機械工程學系
Keywords:	N/A
Date:	1995
Issue Date:	2010-06-29 18:06:43 (UTC+8)
Publisher:	中央大學
Abstract:	This article discusses the application of orthogonal neural networks to detect collisions between multiple robot manipulators that work in an overlapped space. By applying an expansion/shrinkage algorithm, the problem of collision detection between arms is transformed into that among cylinders (or rectangular solids) and line segments. This mapping simplifies the collision detection problem and thus neural networks can be applied to solve it. The property of parallel processing enables neural networks to detect collisions rapidly. A single-layer orthogonal neural network is developed to avert the problems of conventional multilayer feedforward neural networks such as initial weights and the number of layers and processing elements. This orthogonal neural network can approximate various functions and is used to calculate forward solution and to detect collisions. An efficient neural network system for collision detection is also developed. (C) 1995 John Wiley & Sons, Inc.
Relation:	JOURNAL OF ROBOTIC SYSTEMS
Appears in Collections:	[Graduate Institute of Mechanical Engineering] journal & Dissertation

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