A toolkit for robotic spraying simulation

This paper introduces a Robotic Spraying Simulation (RSS) Toolkit designed to optimize material deposition in robotic fabrication, with a focus on tubular geometries and complex spatial structures. A key challenge in robotic spraying is inconsistent material distribution, caused by overlaps, uneven spray patterns, and substrate geometry, which can lead to structural weaknesses and inefficiencies. To address this, the Toolkit employs a high-performance simulation framework that models the spraying process using ray-casting and GPU-accelerated computations. The Toolkit delivers rapid results—simulating a 60-second spraying process in just a few seconds, making it highly practical for iterative optimization. The simulation extracts key performance metrics, including material deposition efficiency and uniformity, to assess the effectiveness of path-planning. Experimental validation with cellulose–casein composites confirms the simulation’s accuracy, demonstrating alignment between digital predictions and physical outcomes. This work advances data-driven fabrication by providing a robust framework for process understanding and control, enabling designers and engineers to refine robotic spraying strategies for predictable and high-quality results.