ERI: Prediction-Enabled Safe and Productive Human-Robot Collaboration in Dynamic and Uncertain Construction Workspaces

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

Construction industry has long been challenged by low productivity and high rates of injuries and fatalities, while the persistent workforce aging and labor shortage further exacerbate the challenges. Robotic construction has been increasingly recognized as a promising solution to relieve human workers from dangerous and physically demanding tasks and thus improving productivity and occupational health and safety.

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. This project will advance the understanding of the bi-directional influence and interaction between human and robot when collaborating in complex construction activities in dynamic environments by developing new methods to predict worker intention from their behavior and adaptively plan robot’s motion considering both job contexts and human dynamics.

The developed human-robot collaboration mechanism will provide new opportunities for proactive, adaptive, safe, and productive robotic construction solutions. The new knowledge gained from this research will provide insights on human-robot collaboration in complex and dynamic environments, and can be generalized to other domains, such as search and rescue, agriculture, and manufacturing.

This project also involves education and outreach activities to promote broad participation of students, especially those from underrepresented groups, through which younger generations will be motivated to enter the industry and trained with new capabilities to work with high technologies to increase competitiveness of the industry and the nation.

This research will lead to a new paradigm for safe and productive human-robot collaboration in dynamic and uncertain construction workspaces by predicting human intention and motion, estimating robot subtasks, and generating optimal motion trajectory, considering the complex worker-robot-workpiece-workspace interaction. Using formwork construction as the working context, this project will 1) develop new deep learning models that integrate human behavior data and contextual information (e.g., workspace configuration) to predict worker’s intention and motion and associated uncertainties, and 2) develop new layered control mechanism to infer robot’s task in multi-process construction operation and generate optimal motion trajectory considering semantic construction knowledge, predicted human dynamics and associated uncertainties to achieve both collaboration safety and productivity.

Extensive experiments will be conducted to collect multimodal human behavior data when collaborating with robots and to test the performance of developed technologies.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.