Robotization of Miniature-Scale Radio-Controlled Excavator: A New Medium for Construction- Specific DNN Data Generation

Abstract

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.