A Physics-Informed Action Selection Framework for Robotic Heating

Abstract Robots are being considered for performing external heating of components in manufacturing applications. This paper presents a physics-aware action selection policy that employs forward simulation with a branch and bound search to efficiently determine the best action sequence to position the heating tool. We take inspiration from physics-informed machine learning and present a parameter learning graph-based modeling framework that enables the robot to predict the temperature evolution of the surface of interest with respect to time. We also present a state transition model to describe how the thermal characteristics of the system change based on the heating tool’s position. We demonstrate our proposed robotic heating action selection approach for the composite layup process on an industrial tool. This work demonstrates the usefulness of physics-inspired machine learning in a real-world application.