KLOBUČAR, Rok ;ČUŠ, Jure ;ŠAFARIČ, Riko ;BREZOČNIK, Miran . Uncalibrated visual servo control with neural network. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 54, n.9, p. 619-627, august 2017. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/uncalibrated-visual-servo-control-with-neural-network/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/.
Klobučar, R., Čuš, J., Šafarič, R., & Brezočnik, M. (2008). Uncalibrated visual servo control with neural network. Strojniški vestnik - Journal of Mechanical Engineering, 54(9), 619-627. doi:http://dx.doi.org/
@article{.,
author = {Rok Klobučar and Jure Čuš and Riko Šafarič and Miran Brezočnik},
title = {Uncalibrated visual servo control with neural network},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {54},
number = {9},
year = {2008},
keywords = {robots; neural networks; visual servoing; parallel manipulators; },
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.},
issn = {0039-2480}, pages = {619-627}, doi = {},
url = {https://www.sv-jme.eu/sl/article/uncalibrated-visual-servo-control-with-neural-network/}
}
Klobučar, R.,Čuš, J.,Šafarič, R.,Brezočnik, M. 2008 August 54. Uncalibrated visual servo control with neural network. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 54:9
%A Klobučar, Rok %A Čuš, Jure %A Šafarič, Riko %A Brezočnik, Miran %D 2008 %T Uncalibrated visual servo control with neural network %B 2008 %9 robots; neural networks; visual servoing; parallel manipulators; %! Uncalibrated visual servo control with neural network %K robots; neural networks; visual servoing; parallel manipulators; %X 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. %U https://www.sv-jme.eu/sl/article/uncalibrated-visual-servo-control-with-neural-network/ %0 Journal Article %R %& 619 %P 9 %J Strojniški vestnik - Journal of Mechanical Engineering %V 54 %N 9 %@ 0039-2480 %8 2017-08-21 %7 2017-08-21
Klobučar, Rok, Jure Čuš, Riko Šafarič, & Miran Brezočnik. "Uncalibrated visual servo control with neural network." Strojniški vestnik - Journal of Mechanical Engineering [Online], 54.9 (2008): 619-627. Web. 20 Dec. 2024
TY - JOUR AU - Klobučar, Rok AU - Čuš, Jure AU - Šafarič, Riko AU - Brezočnik, Miran PY - 2008 TI - Uncalibrated visual servo control with neural network JF - Strojniški vestnik - Journal of Mechanical Engineering DO - KW - robots; neural networks; visual servoing; parallel manipulators; N2 - 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. UR - https://www.sv-jme.eu/sl/article/uncalibrated-visual-servo-control-with-neural-network/
@article{{}{.},
author = {Klobučar, R., Čuš, J., Šafarič, R., Brezočnik, M.},
title = {Uncalibrated visual servo control with neural network},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {54},
number = {9},
year = {2008},
doi = {},
url = {https://www.sv-jme.eu/sl/article/uncalibrated-visual-servo-control-with-neural-network/}
}
TY - JOUR AU - Klobučar, Rok AU - Čuš, Jure AU - Šafarič, Riko AU - Brezočnik, Miran PY - 2017/08/21 TI - Uncalibrated visual servo control with neural network JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 54, No 9 (2008): Strojniški vestnik - Journal of Mechanical Engineering DO - KW - robots, neural networks, visual servoing, parallel manipulators, N2 - 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. UR - https://www.sv-jme.eu/sl/article/uncalibrated-visual-servo-control-with-neural-network/
Klobučar, Rok, Čuš, Jure, Šafarič, Riko, AND Brezočnik, Miran. "Uncalibrated visual servo control with neural network" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 54 Number 9 (21 August 2017)
Strojniški vestnik - Journal of Mechanical Engineering 54(2008)9, 619-627
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
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.