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.
This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) Awards through the Discovery Grant Funded by the Development and Evaluation of Real-Like Synthetic Construction Images to Enhance the Performances of Deep Neural Network for Construction Applications under Grant RGPIN-2022-04429; and in part by the Collaborative Research and Development Grants, Building Information Modeling (BIM)-Driven Productivity Improvements for the Canadian Construction Industry under Grant 530550-2018.
