Neural approach to visual servoing for robotic hand eye coordination

Kuhn, D. C. S.; Buessler, Jean-Luc; Urban, Jean-Philippe

doi:10.1109/icnn.1995.487731

Cited by 14 publications

(8 citation statements)

References 5 publications

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“…This is for ensuring the hand-mounted eye (CCD2) can see the work piece after robot coarse position. Decouple the fuzzy rule (4), we get (2) R U: if a is neara' ,then 0 = 01', and if r is nearri, then0 02 ,03 03 ,05 05 X…”

Section: Fig 2 Imagel and Fuzzy Segmentmentioning

confidence: 99%

“…However, the high approximation performance of the net needs highly complicated structure and large number of learning samples, which makes it hard for one neural network to be sufficient for a large workspace. To solve this problem, Hashimoto, Kubota and Harashima [1] used a two level back propagation (BP) networks to approximate the Jacobian for global and local mapping; Kuhn, Buessler and Urban [2] utilized a set of adaptive linear (ADALINE) networks to approximate the inverse image Jacobian and a self-organizing map (SOM) network to select the ADALINE matrix; Jiang, Li and Chen [3] presented a distributed neural network structure where each local network is responsible for a short segment of the demonstrated trajectory. These researches are all based on hierarchical control scheme, where a global network decides which local networks may be activated.…”

Section: Introductionmentioning

confidence: 99%

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Visual Servoing Control Based on Fuzzy Behavior and Neural Networks

Liu

Fang

2006

The Proceedings of the Multiconference on "Computational Engineering in Systems Applications"

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This paper proposed a visual servoing control scheme for positioning a robot manipulator. Similar to the behaviour of human, this scheme is divided into two steps: firstly a fuzzy logic-based visual servoing controller is used to guide the gripper into the neighbourhood of the object, and then one local neural network is applied to position the gripper in a desired pose. The whole control needs no calibration of the robot and the cameras and can utilize humans' experience and has high precision. Simulation results on a 6 DOF industrial robot show the validity and effectiveness of the proposed control scheme.

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Section: Fig 2 Imagel and Fuzzy Segmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visual Servoing Control Based on Fuzzy Behavior and Neural Networks

Liu

Fang

2006

The Proceedings of the Multiconference on "Computational Engineering in Systems Applications"

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“…signal and image processing [13-141, high energy physics [15], control [16], robotics [17]). They potentially could provide powerful and compact solutions suitable for space applications such as spacecraft docking, satellite image processing, data classification [18] and traffic control for high speed communication networks (e.g.…”

Section: Asic Principles and Applications Of A " Smentioning

confidence: 99%

SEU experiments on an Artificial Neural Network implemented by means of digital processors

Velazco¹,

Assoum²,

Cheynet³

et al. 1996

IEEE Trans. Nucl. Sci.

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The SEU sensitivity of an Artificial Neural Network intended to be used in space to detect "protonic whistlers" is investigated. We evaluate its behaviour in the presence of SEU-like faults for a hardware implementation, associating a general purpose microprocessor to a dedicated neural processor. Experimental results (SEU simulations and heavy ion ground tests) show the robustness of this implementation

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“…The high computation speed and general modelling capability of neural networks are very attractive properties for nonlinear compensation problems, as indeed robot control problems are. Hashimoto et al utilized two BP networks -one global and one local -to approximate the Jacobian mapping [6,7]; Kuhn et al used an adaptive linear (ADALINE) network to approximate the inverse image Jacobian and a self-organizing map (SOM) network to select the ADALINE matrix [8]. However, the significance of visual servoing is its flexibility, but the nature of offline learning critically weakens this advantage.…”

Section: Introductionmentioning

confidence: 99%

Image Jacobian Matrix Estimation Based on Online Support Vector Regression

Mao

Huang

Wang

2012

International Journal of Advanced Robotic Systems

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Research into robotics visual servoing is an important area in the field of robotics. It has proven difficult to achieve successful results for machine vision and robotics in unstructured environments without using any a priori camera or kinematic models. In uncalibrated visual servoing, image Jacobian matrix estimation methods can be divided into two groups: the online method and the offline method. The offline method is not appropriate for most natural environments. The online method is robust but rough. Moreover, if the images feature configuration changes, it needs to restart the approximating procedure. A novel approach based on an online support vector regression (OL-SVR) algorithm is proposed which overcomes the drawbacks and combines the virtues just mentioned.

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Neural approach to visual servoing for robotic hand eye coordination

Cited by 14 publications

References 5 publications

Visual Servoing Control Based on Fuzzy Behavior and Neural Networks

Visual Servoing Control Based on Fuzzy Behavior and Neural Networks

SEU experiments on an Artificial Neural Network implemented by means of digital processors

Image Jacobian Matrix Estimation Based on Online Support Vector Regression

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