Version 1
: Received: 14 November 2023 / Approved: 22 November 2023 / Online: 22 November 2023 (15:14:45 CET)
How to cite:
Kariuki, S.; Wanjau, E.; Muchiri, I.; Njeri, W.; Muguro, J.; Sasaki, M. Enhancing Palletizing and Shape Drawing Using Image Processing on Parallel and Serial Link Manipulators. Preprints2023, 2023111396. https://doi.org/10.20944/preprints202311.1396.v1
Kariuki, S.; Wanjau, E.; Muchiri, I.; Njeri, W.; Muguro, J.; Sasaki, M. Enhancing Palletizing and Shape Drawing Using Image Processing on Parallel and Serial Link Manipulators. Preprints 2023, 2023111396. https://doi.org/10.20944/preprints202311.1396.v1
Kariuki, S.; Wanjau, E.; Muchiri, I.; Njeri, W.; Muguro, J.; Sasaki, M. Enhancing Palletizing and Shape Drawing Using Image Processing on Parallel and Serial Link Manipulators. Preprints2023, 2023111396. https://doi.org/10.20944/preprints202311.1396.v1
APA Style
Kariuki, S., Wanjau, E., Muchiri, I., Njeri, W., Muguro, J., & Sasaki, M. (2023). Enhancing Palletizing and Shape Drawing Using Image Processing on Parallel and Serial Link Manipulators. Preprints. https://doi.org/10.20944/preprints202311.1396.v1
Chicago/Turabian Style
Kariuki, S., Joseph Muguro and Minoru Sasaki. 2023 "Enhancing Palletizing and Shape Drawing Using Image Processing on Parallel and Serial Link Manipulators" Preprints. https://doi.org/10.20944/preprints202311.1396.v1
Abstract
The integration of robotics and image processing has led to the realization of robot autonomy in dynamic environments through the provision of visual feedback. This paper presents the application of parallel and open-link robots in palletizing and shape drawing tasks as enhanced by visual feedback from image processing. In determining the set of joint angles that could be used to reach the desired position and orientation of the end effector, the geometric approach in which the spatial geometry of the robotic arms was decomposed into several plane geometry problems was employed. Image processing techniques were used to enhance the performance of the robotic manipulators. In one approach, Color-based segmentation was used to distinguish between different objects in the workspace by using predefined color markers as references in the L*a*b color space. Classification of each pixel in the workspace image was then done by calculating the Euclidean distance between that pixel and a predefined color marker. A second approach employed Edge detection to identify the boundaries of objects within the workspace image by employing the Hough Transform mathematical model to detect the abrupt changes in the image brightness pixel-wise. The pixel locations from Hough were then sorted sequentially to outline the detected object. The integration of image processing with the robotic tasks was expected to improve the precise detection of the position of objects as well as the outline of geometric shapes. The incorporation of visual feedback allowed for dynamic robot manipulation in which prior knowledge of the workspace was not requisite. This led to improved pick and place as well as shape detection as applied in palletizing and shape drawing tasks actuated by the parallel and serial link manipulators, respectively.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
