ANN Based Robotic Arm Visual Servoing Nonlinear System

Al-Junaid, Hessa

doi:10.1016/j.procs.2015.08.405

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…However, the convergence of the method was very slow, and the trajectory of the end effector was not sufficiently smooth. H. A. Junaid designed a four-layer artificial neural network for training between the image feature space and manipulator motion space [22], which reduced the IBVS system's calculation time. However, the disadvantage of this method was that the neural network needed to reacquire the training samples and train for different robot models.…”

Section: Image-based Visual Servoingmentioning

confidence: 99%

Robot Manipulator Visual Servoing via Kalman Filter- Optimized Extreme Learning Machine and Fuzzy Logic

2022

KSII TIIS

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Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

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Section: Image-based Visual Servoingmentioning

confidence: 99%

Robot Manipulator Visual Servoing via Kalman Filter- Optimized Extreme Learning Machine and Fuzzy Logic

2022

KSII TIIS

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“…[17,18] In some visual servoing tasks, NNs are applied in robot control to deal with the uncertainties in kinematics, dynamics, Jacobian matrices, and model uncertainties during object manipulation. [19][20][21][22][23] Kelly et al [24] deliberated visual servoing of a robot manipulator in a fixed-camera structure in which the NNs estimate the inverse perceptual kinematic mapping. The continuous feedback provided by the vision sensors increases the robot manipulator flexibility when the adaptation capability of the NNs is combined.…”

Section: Introductionmentioning

confidence: 99%

Neural network‐assisted robotic vision system for industrial applications

Krishnan

Ashok

2021

Asian Journal of Control

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This paper investigates the performance of neural networks in positioning an industrial robot manipulator based on image feedback. Visual servoing regulates the pose of the robot manipulator in accordance with the target image data. As the servoing proceeds, the end-effector positions to its final pose as the image feature error exponentially decreases to zero. In this paper, the trained network moves the robot manipulator from an arbitrary initial pose to an intermediate posture based on the reference image features given. Then, fine-tuning based on the traditional visual servoing method is performed to achieve an accurate pick-and-place task with minimum iterations. The experimental results prove the capability of the neural network architectures to predict the desired pose using local image descriptor, corner points. This paper investigates the performance of two neural network designs applied to visual positioning and compares them with the traditional image-based visual servoing (IBVS) method in terms of execution time.

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“…Al-Junaid realized ANN based control of a nonlinear system with visual servoing. ANN was used to control a 6-axis robot arm in his study [2]. Feng et.al.…”

Section: Introductionmentioning

confidence: 99%

Visual Servoing Application for Inverse Kinematics of Robotic Arm Using Artificial Neural Networks

Ak¹,

Topuz²,

Erşan³

2019

STUD INFORM CONTROL

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This paper presents novel approach for a visual servoing application of six axis robotic arm. Basic imageprocessing techniques were used for object recognition and position determination of robotic arm. The inverse kinematics solution of the robot arm was performed with artificial neural networks. Afterwards the robot's inverse kinematics solution was completed, the determined joint-angle values were used to control the robot arm. Performance of radial basis function network (RBF) and multilayer perceptron (MLP) were also compared.

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ANN Based Robotic Arm Visual Servoing Nonlinear System

Cited by 7 publications

References 5 publications

Robot Manipulator Visual Servoing via Kalman Filter- Optimized Extreme Learning Machine and Fuzzy Logic

Robot Manipulator Visual Servoing via Kalman Filter- Optimized Extreme Learning Machine and Fuzzy Logic

Neural network‐assisted robotic vision system for industrial applications

Visual Servoing Application for Inverse Kinematics of Robotic Arm Using Artificial Neural Networks

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