PLASTR: Planning for Autonomous Sampling-Based Trowelling

Abstract

Plaster is commonly used in the construction industry to finish walls and ceilings, but the application is labor-intensive and physically strenuous, which motivates the need for automation. We present PLASTR, a receding horizon optimization-based planning algorithm for robotic plaster trowelling. It samples trowelling sequence rollouts from a new plaster simulator and weights them according to the flatness of the finished wall. The proposed simulator approximates the real-world plastertrowel interaction adequately while allowing execution orders of magnitude faster than real-time. We evaluate PLASTR in simulation and on a real-world test setup and compare it to two handcrafted heuristic baseline algorithms. PLASTR performs equal to or better than the best heuristic in terms of material coverage for both simulated and real-world experiments while being 50 % more efficient in terms of trowelled distance.