Humanoid robot control system utilizing cost-oriented automation (COA) and edge detection
Humanoid robots are machines designed to resemble the human body. They emulate specific aspects of human physiology, cognition, and social behavior to facilitate perception, processing, and action. One of the world’s most notable humanoid robot competitions is the HuroCup organized by The Federation...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/31/e3sconf_iccsei2023_01012.pdf |
| _version_ | 1797248986654965760 |
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| author | Prabowo Aditya Bayu Pratomo Awang Hendrianto Ahmad M.S. Hendriyawan |
| author_facet | Prabowo Aditya Bayu Pratomo Awang Hendrianto Ahmad M.S. Hendriyawan |
| author_sort | Prabowo Aditya Bayu |
| collection | DOAJ |
| description | Humanoid robots are machines designed to resemble the human body. They emulate specific aspects of human physiology, cognition, and social behavior to facilitate perception, processing, and action. One of the world’s most notable humanoid robot competitions is the HuroCup organized by The Federation of International Sports Association (FIRA). Although the race hosts various categories, this study only considers the obstacle run. This study has multiple objectives, including enhancing the ability of the humanoid robot’s vision system to detect objects using the Edge Detection algorithm. Additionally, the study aims to integrate the Cost-Oriented Automation (COA) and Edge Detection algorithms to enable the robot to detect and avoid obstacle objects. The COA algorithm serves a role in the robot’s body structure, while the Edge Detection algorithm detects objects through the Canny operator’s edge detection capabilities within the robot’s visual range. The Canny operator functions to reduce edge ambiguity for improved object detection. The results of the test indicate that in images with dark light intensity conditions in the HSV (Value Channel) color space, the average detection accuracy of the system reaches 71.43%. The detection accuracy increases to 82.86% in images with bright light intensity conditions. However, in images with dark light intensity conditions in the RGB (Blue Channel) color space, the detection accuracy is 50%, and it increases to 61.42% in images with bright light intensity conditions. The data confirms that using HSV color space images (Value Channel) provides better detection accuracy results than using RGB color space images (Blue Channel). However, the accuracy of robot movement remains a challenge that requires consideration. The implementation of the Cost-Oriented Automation (COA) and Edge Detection algorithms was able to successfully detect all box-shaped objects; however, the robot’s movements were inaccurate in avoiding obstacles. This was due to the robot’s unstable balance, and there were a few servos in its legs that stopped themselves, resulting in undesired movement. Therefore, the implementation of the Cost-Oriented Automation (COA) algorithm to the robot frame is suboptimal. Further refinement is necessary to improve the accuracy of robot movements. This requires replacing several faulty components, including servos and robot frames, to enhance the system’s overall performance, especially robot movements. |
| first_indexed | 2024-04-24T20:23:18Z |
| format | Article |
| id | doaj.art-fd11ddfeee5843c8b2d6620baa99a273 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-24T20:23:18Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-fd11ddfeee5843c8b2d6620baa99a2732024-03-22T07:55:39ZengEDP SciencesE3S Web of Conferences2267-12422024-01-015010101210.1051/e3sconf/202450101012e3sconf_iccsei2023_01012Humanoid robot control system utilizing cost-oriented automation (COA) and edge detectionPrabowo Aditya Bayu0Pratomo Awang Hendrianto1Ahmad M.S. Hendriyawan21Informatics, Universitas Pembangunan Nasional “Veteran”1Informatics, Universitas Pembangunan Nasional “Veteran”Electrical Engineering, Yogyakarta University of TechnologyHumanoid robots are machines designed to resemble the human body. They emulate specific aspects of human physiology, cognition, and social behavior to facilitate perception, processing, and action. One of the world’s most notable humanoid robot competitions is the HuroCup organized by The Federation of International Sports Association (FIRA). Although the race hosts various categories, this study only considers the obstacle run. This study has multiple objectives, including enhancing the ability of the humanoid robot’s vision system to detect objects using the Edge Detection algorithm. Additionally, the study aims to integrate the Cost-Oriented Automation (COA) and Edge Detection algorithms to enable the robot to detect and avoid obstacle objects. The COA algorithm serves a role in the robot’s body structure, while the Edge Detection algorithm detects objects through the Canny operator’s edge detection capabilities within the robot’s visual range. The Canny operator functions to reduce edge ambiguity for improved object detection. The results of the test indicate that in images with dark light intensity conditions in the HSV (Value Channel) color space, the average detection accuracy of the system reaches 71.43%. The detection accuracy increases to 82.86% in images with bright light intensity conditions. However, in images with dark light intensity conditions in the RGB (Blue Channel) color space, the detection accuracy is 50%, and it increases to 61.42% in images with bright light intensity conditions. The data confirms that using HSV color space images (Value Channel) provides better detection accuracy results than using RGB color space images (Blue Channel). However, the accuracy of robot movement remains a challenge that requires consideration. The implementation of the Cost-Oriented Automation (COA) and Edge Detection algorithms was able to successfully detect all box-shaped objects; however, the robot’s movements were inaccurate in avoiding obstacles. This was due to the robot’s unstable balance, and there were a few servos in its legs that stopped themselves, resulting in undesired movement. Therefore, the implementation of the Cost-Oriented Automation (COA) algorithm to the robot frame is suboptimal. Further refinement is necessary to improve the accuracy of robot movements. This requires replacing several faulty components, including servos and robot frames, to enhance the system’s overall performance, especially robot movements.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/31/e3sconf_iccsei2023_01012.pdf |
| spellingShingle | Prabowo Aditya Bayu Pratomo Awang Hendrianto Ahmad M.S. Hendriyawan Humanoid robot control system utilizing cost-oriented automation (COA) and edge detection E3S Web of Conferences |
| title | Humanoid robot control system utilizing cost-oriented automation (COA) and edge detection |
| title_full | Humanoid robot control system utilizing cost-oriented automation (COA) and edge detection |
| title_fullStr | Humanoid robot control system utilizing cost-oriented automation (COA) and edge detection |
| title_full_unstemmed | Humanoid robot control system utilizing cost-oriented automation (COA) and edge detection |
| title_short | Humanoid robot control system utilizing cost-oriented automation (COA) and edge detection |
| title_sort | humanoid robot control system utilizing cost oriented automation coa and edge detection |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/31/e3sconf_iccsei2023_01012.pdf |
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