Human-Inspired Planning and Control of Shotcrete Robots based on Dynamical Systems Mapping

Performing shotcrete operations at construction sites can be hazardous to humans and inefficient. Robots can offer a safer and more efficient alternative to assist in these tasks. We present a new planning strategy for shotcrete robots, including both the spraying and surface finishing phases, that can plan for a general target area, whether flat or complexly curved. Our method uses learning from demonstrations and dynamical systems concepts to enable reactive and adaptive planning for robots, allowing them to effectively handle disturbances. We evaluated the effectiveness of the proposed planning and control framework in a laboratory setup using a velocity-controlled robot and curved targets both in the spraying and polishing phases. The results demonstrate the effectiveness of the proposed approach.