Data-driven Approach for Context-aware Human-Robot Collaboration in Future Construction

Jiannan Cai, Ph.D.

Assistant Professor

School of Civil and Environmental Engineering, and Construction Management

The University of Texas at San Antonio

2/3/2023

11 AM CST – 12PM CST

https://utsa.webex.com/utsa/j.php?MTID=m9c9f14f98a0738fb93ecf37e6474e8fd

Construction is labor-intensive, physically demanding, and hazardous work, while the persistent workforce aging and labor shortage further exacerbates the challenges. Construction robotics has been increasingly recognized as a promising solution to relieve human workers from repetitive and physically demanding tasks and thus improving work productivity and workers’ occupational health. However, the unstructured and dynamic workspaces and the diverse and complex construction activities make it extremely difficult to apply industrial robots that are traditionally pre-programmed to conduct a single task in a fixed working environment. Without eliminating construction workers, collaborative robots in construction must be designed to cognitively team up with workers, in order to assist humans in repetitive and physically demanding tasks while leveraging human expertise to handle unexpected and complex situations. In this presentation, Dr. Jiannan Cai will introduce the opportunities and challenges in construction robotics, explain their findings from human-robot collaboration experiments, and discuss their research efforts in developing data-driven approaches by coupling sensing, computer vision, deep learning, and robotics, to promote safe and efficient human-robot collaboration in future construction.