Uncalibrated visual servo control with neural network

Rok Klobučar (Author), Jure Čas (Author), Riko Šafarič (Author), Miran Brezočnik (Author)

Abstract

Research into robotics visual servo systems is an important content in the robotics field. This paper describes a control approach for a robotics manipulator. In this paper, a multilayer feedforward network is applied to a robot visual servo control problem. The model uses new neural network architecture and a new algorithm for modifying neural connection strength. No a-prior knowledge is required of robot kinematics and camera calibration. The network is trained using an end-effector position. After training, performance is measured by having the network generate joint-angles for arbitrary end effector trajectories. A 2-degrees-of-freedom (DOF) parallel manipulator was used for the study. It was discovered that neural networks provide a simple and effective way of controlling robotic tasks. This paper explores the application of a neural network for approximating nonlinear transformation relating to the robotćs tip-position, from the image coordinates to its joint coordinates. Real experimental examples are given to illustrate the significance of this method. Experimental results are compared with a similar method called the Broyden method, for uncalibrated visual servo-control.

Keywords

robots;neural networks;visual servoing;parallel manipulators;

Data

Language:	English
Year of publishing:	2008
Typology:	1.01 - Original Scientific Article
Organization:	UM FS - Faculty of Mechanical Engineering
Publisher:	= Association of Mechanical Engineers and Technicians of Slovenia et al.
UDC:	681.5:007.52
COBISS:	12712214
ISSN:	0039-2480
Parent publication:	Strojniški vestnik
Views:	1183
Downloads:	94
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary title:	Vodenje robota s pomočjo računalniškega vida in nevronske mreže
Secondary abstract:	Raziskave s področja vodenja robota s pomočjo računalniškega vida so zelo pomembne. V prispevku je opisano vodenje robotskega manipulatorja. Za vodenje s pomočjo umetnega vida je uporabljena polno povezana usmerjena nevronska mreža. Uporabljena je povsem nova arhitektura nevronske mreže in nov algoritem nevronske mreže. Natančno poznavanje kinematike robota in kalibracija kamere nista potrebna. Nevronska mreža se uči s pomočjo vrha robota. Po učenju smo testirali natančnost nevronske mreže. Nevronska mreža je morala generirati notranje koordinate glede na zunanjo trajektorijo. Eksperiment je bil izveden na paralelnem manipulatorju z dvema prostostnima stopnjama. Pokazali smo, da lahko s pomočjo takšne arhitekture nevronske mreže robota opravlja zadane naloge. V prispevku je predstavljena uporaba nevronske mreže za ocenitev nelinearne transformacije med vrhom robota v sliki in notranjih koordinat. Eksperimentalni primeri potrjujejo zanesljivost predlagane metode. Eksperimentalne rezultate smo primerjali s podobno metodo, imenovano Broydenova metoda za nekalibrirano vodenje robota s pomočjo umetnega vida.
Secondary keywords:	robotika;roboti;nevronske mreže;računalniški vid;paralelni manipulatorji;vizualizacijski servo sistemi;
URN:	URN:NBN:SI
Type (COBISS):	Not categorized
Pages:	str. 619-627
Volume:	ǂLetn. ǂ54
Issue:	ǂšt. ǂ9
Chronology:	2008
ID:	1736681