Methodology
Traditional construction industries are faced with low production efficiency, high construction costs, and poor production. In recent years, research on automation in construction and monitoring tasks has advanced rapidly. Automation of an on-site crane requires sensor feedback to its closed-loop control system. For example, local displacement or angular sensors should be used to measure the movement of actuators on the crane, while global monitoring devices such as drones or cameras can be used to capture the surrounding information. This research aims to develop a novel sensory system using computer vision and point cloud techniques for construction site mapping, detection and tracking of construction equipment, materials, worker activities, construction safety monitoring, construction progress monitoring, etc.
Methodology
[1]. Pan, X., Yang, T.Y., Liu, R., Xiao Y., Xie, F. (2024). A computer vision and point cloud-based monitoring approach for automated construction tasks using full-scale robotized mobile cranes. Journal of Intelligent Construction.
[2]. Xiao Y., Yang T. Y., Pan, X., Xie, F., Chen, Z. (2023). A reinforcement learning based construction material supply strategy using robotic crane and computer vision for building reconstruction after an earthquake. Canadian Conference - Pacific Conference on Earthquake Engineering (CCEE-PCEE) 2023, Vancouver, British Columbia, Canada.
[3]. Xiao, Y., Pan, X., Tavasoli, S., M. Azimi, Y. Bao, Noroozinejad Farsangi E., Yang T.Y. (2023) “Autonomous inspection and construction of civil infrastructure using robots.” Automation in Construction Toward Resilience, edited by Ehsan Noroozinejad Farsangi, Mohammad Noori, Tony T.Y. Yang, Paulo B. Lourenço, Paolo Gardoni Izuru Takewaki, Eleni Chatzi, Shaofan Li.