The objective of this project is to investigate new perceptual, modeling, and planning methods to enable construction co-robots to overcome positioning and geometric uncertainties in their workspaces and adaptively perform useful work. First, we will explore the sensing modalities and strategies that best enable co-robots to perceive objects and features in highly unstructured and cluttered environments typical of construction sites. Second, we will investigate modeling techniques to allow robots to convert raw sensory data into meaningful and actionable models of rigid and semi-rigid workpieces encountered in construction. Third, we will explore planning techniques to enable robots to generate adaptive plans based on their understanding of the objects in their environment and the human co-workers’ instructions, their proximity to human co-workers in the workspace, and their estimation of the human co-workers’ trust. Finally, through integration and experimentation in laboratory and construction site environments, we will evaluate performance levels attainable by human-robot teams in performing work adaptively.
Funding: $30K (2022)
Goal: This research will advance the scalability of co-robots working in unstructured industrial environments by developing perceptual, modeling, and planning capabilities needed to adapt to unexpected workspace geometries and perform work collaboratively with humans.
Token Investors: Vineet Kamat, Carol Menassa
Project ID: 1018