APPcS - Autonomous Process Planning for Complex Surfaces

Many advanced manufacturing and maintenance processes require precise handling of complex geometric components, particularly in aerospace applications where precision, repeatability, and efficiency are paramount. Significant advances have been made automating many of these processes on parts with simple geometries. However, these advanced robotic systems still struggle with complex geometries, as these geometries must be reasoned about differently depending on the process.This research effort aims to expanding the ability for existing and future advanced robotic systems to process parts with complex geometries through advances in model generation, process-specific reasoning, and simulation capabilities. The first objective focuses on developing a suite of model generation tools that allow operators with modest experience to accurately create digital representations of complex components. This involves refining model requirements, researching state-of-the-art mesh generation techniques, and implementing a robust model generation framework. The ability to rapidly and accurately generate models is essential to bring this capability to the organic sustainment and manufacturing base.The second objective enhances process-specific reasoning by modifying model definitions and planning software to incorporate critical manufacturing and maintenance constraints. This involves adapting digital models to reflect real-world process variables, reducing the need for manual intervention, and improving the overall efficiency of automation systems. The research will explore methodologies for process-informed path planning, ensuring that manufacturing workflows can intelligently adapt to the diverse challenges posed by complex geometries.The final objective focuses on expanding simulation capabilities to improve predictive accuracy and operator interaction. Existing simulation frameworks will be enhanced to account for off-nominal conditions such as variations in coating application and geometric deviations. The research will integrate high-fidelity process simulations that allow operators to assess and refine work plans before execution, ensuring a seamless transition from digital planning to physical manufacturing. A key component of this effort is the development of intuitive user interfaces that enable human operators to review, modify, and approve process plans efficiently.The culmination of this effort will be an end-to-end demonstration of a fully integrated, process-informed planning system applied to a complex aerospace component. This demonstration will validate the ability to generate digital models, plan manufacturing workflows, simulate process execution, and incorporate human expertise where necessary. By bridging the gap between simulation and real-world application, this research will contribute to increased automation efficiency, reduced operational costs, and improved reliability in advanced manufacturing and sustainment processes.