Robust neural control of robot-camera visual tracking

Cát, Phạm Thượng; Minh, N.

doi:10.1109/icca.2009.5410315

Cited by 6 publications

(1 citation statement)

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“…Many visual servoing systems have been studied and developed. Most of all use one [3,6] or two cameras to track moving targets. With stereo visual servoing system [7,8,13] the posture of the target can be determined in 3D Cartesian coordinates.…”

Section: Introductionmentioning

confidence: 99%

A new control method for stereo visual servoing system with Pan-tilt platform

Chung¹,

Cát²

2015

JCC

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This paper proposes a new control method for Pan-Tilt stereo camera system to track a moving object when there are many uncertainties in the parameters of both camera and Pan-Tilt platform. If a pair of cameras placed on the Pan-Tilt robot, it is unnecessary for its installation location to be determined accurately. Assuming that the optical parameters like focal length of two cameras in the simulation are the same. A two degree of freedom Pan-Tilt platform holding camera has many uncertain parameters such as inertial moment, Jacobian matrix, the friction and noise impact, etc. The proposed control algorithm is highly adaptive and robust due to the use of a compensating neural network with on-line learning rule. The asymptotic stability of overall control system is proved by Lyapunov's stability method.

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Section: Introductionmentioning

confidence: 99%

A new control method for stereo visual servoing system with Pan-tilt platform

Chung¹,

Cát²

2015

JCC

Self Cite

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Mobile Robot Control Based on 2D Visual Servoing: A New Approach Combining Neural Network With Variable Structure and Flatness Theory

et al. 2021

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This paper focuses on the 2D visual servo-control of a mobile robot using a neural network (NN) with variable structure. The interaction matrix relating camera movement and changes in visual characteristics requires an estimation phase to determine its parameters as well as a camera calibration phase. It is common in applications related to mobile robotics that the robot model contains uncertainties generated by the sliding phenomenon. We suggest online identification, using NN to avoid this problem. The RBF NN is used to estimate the block formed by the interaction matrix and the reverse robot. Since the number of variables to be estimated is large, this can lead to the use of an excessive number of RBFs. We propose to use a single point of the scene which is sufficient to solve the problem. This problem reduction is possible thanks to flatness theory which allows to reduce the number of NN inputs from 8 inputs (4 image points) -generally used in the literature-to 2 (one image point) only. In order to further reduce the complexity of the proposed algorithm, the number of neurons for each layer and for each iteration is optimized. We use a neural network with variable structure to reach this objective. The very encouraging results obtained validate the proposed approach.

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