Optimizing Grasping Precision for Industrial Pick-and-Place Tasks Through a Novel Visual Servoing Approach
Khairidine Benali

TL;DR
This paper presents a novel visual servoing control system that significantly improves grasping precision for industrial robotic pick-and-place tasks by integrating object localization and visual feedback control methods.
Contribution
It introduces a new visual servoing approach combining object localization and control feedback to enhance accuracy in challenging industrial environments.
Findings
Improved grasping accuracy in noisy industrial settings
Effective handling of various object shapes and types
Robust performance despite environmental disturbances
Abstract
The integration of robotic arm manipulators into industrial manufacturing lines has become common, thanks to their efficiency and effectiveness in executing specific tasks. With advancements in camera technology, visual sensors and perception systems have been incorporated to address more complex operations. This study introduces a novel visual serving control system designed for robotic operations in challenging environments, where accurate object pose estimation is hindered by factors such as vibrations, tool path deviations, and machining marks. To overcome these obstacles, our solution focuses on enhancing the accuracy of picking and placing tasks, ensuring reliable performance across various scenarios. This is accomplished by a novel visual servoing method based on the integration of two complementary methodologies: a technique for object localization and a separate approach for…
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Taxonomy
TopicsAdvanced Vision and Imaging
