Robotization of Miniature-Scale Radio-Controlled Excavator: A New Medium for Construction- Specific DNN Data Generation
Digital management of excavators has seen limited progress due to challenges in artificial intelligence (AI) training. The AI required for digital twinning of excavators necessitates a large volume of diverse imagery data, currently scarce in the construction domain. Moreover, the absence of deploya...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10847859/ |
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| author | Seyedeh Fatemeh Saffari Daeho Kim Byungjoo Choi |
| author_facet | Seyedeh Fatemeh Saffari Daeho Kim Byungjoo Choi |
| author_sort | Seyedeh Fatemeh Saffari |
| collection | DOAJ |
| description | Digital management of excavators has seen limited progress due to challenges in artificial intelligence (AI) training. The AI required for digital twinning of excavators necessitates a large volume of diverse imagery data, currently scarce in the construction domain. Moreover, the absence of deployable robot agents hinders reinforcement learning, impeding task-oriented AI development. In response, we introduce an innovative approach utilizing a miniature-scale, radio-controlled excavator (RC-excavator). This presents a cost-effective method for automated data collection and labeling, as well as interactive reinforcement learning. The RC-excavator’s electric circuit was modified, its motion dynamics were modeled, and it was fully robotized for precise computer-directed motion control. Statistical validation of its motions achieved a Normalized Range Adjusted Accuracy (NRAA) of 99.14% for the bucket, 97.97% for the main arm, and 98.63% for the cabin. This confirms its adequacy for image labeling and task-oriented automation research. |
| format | Article |
| id | doaj-art-b72700fbf9b84a1bb3d50ac0ef8432b5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b72700fbf9b84a1bb3d50ac0ef8432b52025-01-31T00:01:57ZengIEEEIEEE Access2169-35362025-01-0113170541706710.1109/ACCESS.2025.353220310847859Robotization of Miniature-Scale Radio-Controlled Excavator: A New Medium for Construction- Specific DNN Data GenerationSeyedeh Fatemeh Saffari0https://orcid.org/0000-0003-1876-6429Daeho Kim1https://orcid.org/0000-0002-7381-9805Byungjoo Choi2https://orcid.org/0000-0002-3904-4305Department of Civil and Mineral Engineering, University of Toronto, Toronto, ON, CanadaDepartment of Civil and Mineral Engineering, University of Toronto, Toronto, ON, CanadaDepartment of Architectural Engineering, Ajou University, Suwon-si, Gyeonggi-do, Republic of KoreaDigital management of excavators has seen limited progress due to challenges in artificial intelligence (AI) training. The AI required for digital twinning of excavators necessitates a large volume of diverse imagery data, currently scarce in the construction domain. Moreover, the absence of deployable robot agents hinders reinforcement learning, impeding task-oriented AI development. In response, we introduce an innovative approach utilizing a miniature-scale, radio-controlled excavator (RC-excavator). This presents a cost-effective method for automated data collection and labeling, as well as interactive reinforcement learning. The RC-excavator’s electric circuit was modified, its motion dynamics were modeled, and it was fully robotized for precise computer-directed motion control. Statistical validation of its motions achieved a Normalized Range Adjusted Accuracy (NRAA) of 99.14% for the bucket, 97.97% for the main arm, and 98.63% for the cabin. This confirms its adequacy for image labeling and task-oriented automation research.https://ieeexplore.ieee.org/document/10847859/Automated labelingconstruction equipmentdigital twinninginteractive reinforcement learning |
| spellingShingle | Seyedeh Fatemeh Saffari Daeho Kim Byungjoo Choi Robotization of Miniature-Scale Radio-Controlled Excavator: A New Medium for Construction- Specific DNN Data Generation IEEE Access Automated labeling construction equipment digital twinning interactive reinforcement learning |
| title | Robotization of Miniature-Scale Radio-Controlled Excavator: A New Medium for Construction- Specific DNN Data Generation |
| title_full | Robotization of Miniature-Scale Radio-Controlled Excavator: A New Medium for Construction- Specific DNN Data Generation |
| title_fullStr | Robotization of Miniature-Scale Radio-Controlled Excavator: A New Medium for Construction- Specific DNN Data Generation |
| title_full_unstemmed | Robotization of Miniature-Scale Radio-Controlled Excavator: A New Medium for Construction- Specific DNN Data Generation |
| title_short | Robotization of Miniature-Scale Radio-Controlled Excavator: A New Medium for Construction- Specific DNN Data Generation |
| title_sort | robotization of miniature scale radio controlled excavator a new medium for construction specific dnn data generation |
| topic | Automated labeling construction equipment digital twinning interactive reinforcement learning |
| url | https://ieeexplore.ieee.org/document/10847859/ |
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