Version 1
: Received: 23 August 2024 / Approved: 23 August 2024 / Online: 26 August 2024 (02:33:28 CEST)
How to cite:
Li, R.; Assadian, F. Energy Aware Camera Location Search Algorithm for Increasing Precision of Observation in Automated Manufacturing. Preprints2024, 2024081718. https://doi.org/10.20944/preprints202408.1718.v1
Li, R.; Assadian, F. Energy Aware Camera Location Search Algorithm for Increasing Precision of Observation in Automated Manufacturing. Preprints 2024, 2024081718. https://doi.org/10.20944/preprints202408.1718.v1
Li, R.; Assadian, F. Energy Aware Camera Location Search Algorithm for Increasing Precision of Observation in Automated Manufacturing. Preprints2024, 2024081718. https://doi.org/10.20944/preprints202408.1718.v1
APA Style
Li, R., & Assadian, F. (2024). Energy Aware Camera Location Search Algorithm for Increasing Precision of Observation in Automated Manufacturing. Preprints. https://doi.org/10.20944/preprints202408.1718.v1
Chicago/Turabian Style
Li, R. and Farhad Assadian. 2024 "Energy Aware Camera Location Search Algorithm for Increasing Precision of Observation in Automated Manufacturing" Preprints. https://doi.org/10.20944/preprints202408.1718.v1
Abstract
Abstract: Visual servoing technology has been well developed and applied in many automated manufacturing tasks, especially in tools’ pose alignment. To access a full global view of tools, most applications adopt eye-to-hand configuration or eye-to-hand/eye-in-hand cooperation configura-tion in an automated manufacturing environment. All research papers mainly put efforts into developing control and observation architectures in various scenarios, but none of them has dis-cussed the importance of the camera’s location in eye-to-hand configuration. In a manufacturing environment, the quality of camera estimations may vary significantly from one observation loca-tion to another, as the combined effects of environmental conditions result in different noise levels of a single image shot at different locations. In this paper, we propose an algorithm for the camera’s moving policy so that it explores the camera workspace and searches for the optimal location where the images’ noise level is minimized. Also, this algorithm ensures the camera ends up at a subop-timal (if the optimal one is unreachable) location among the locations already searched, with limited energy available for moving the camera. An automated manufacturing application has been sim-ulated and the results show the success of this algorithm’s improvement of observation precision with limited energy.
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.
