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Performance Enhancement in a Two-Camera Visual Servoing System Using Artificial Neural Networks


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DOI: https://doi.org/10.15866/ireaco.v16i4.23663

Abstract


Visual sensory information from cameras for robot guidance and feedback control increases the scope of industrial automation. Heterogeneous workspaces and parallel operations may require the data from more than one sensor. This paper illustrates a regulatory scheme in visual servo control, utilizing the cooperation of two cameras for robot manipulation with a switching approach in configuration and oversight. A supervisory camera placed at a convenient location monitors the workspace such that the end effector camera can extend its field of view in Image Based Visual Servoing (IBVS). The robot is steered by a hybrid control law prior to switching to IBVS in its stable and converging region. Selection of gain factors complementing each other ensures smooth changeover in control laws with smooth velocity profile using a qualitative approach. Alternately, trained Artificial Neural Networks (ANN) improve the performance through prediction servoing over regions of uncertainty, multiple switches and multivariable loop interactions.
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Keywords


Artificial Neural Network; Computational Intelligence; Machine Vision; Regulatory Control; Robot Vision; Visual Servoing

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References


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Product manual-IRB 1200, ABB Robots.


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