Path planning for dual-arm fiber patch placement with temperature loss constraints

Abstract

In the process of composite material molding, the role of robots has become increasingly significant. However, the laying process of composite materials is heavily influenced by factors such as temperature and pressure, which affect the quality of the layup. Currently, existing robot path planning algorithms do not account for temperature constraints, presenting challenges in achieving high-quality composite material layups. This paper proposes the RRT*-MTL (Rapidly-Exploring Random Trees* with Minimum Temperature Loss) path planning algorithm. Firstly, a neural network model for predicting temperature loss is developed. Then, temperature loss is integrated as a constraint into the RRT* (Rapidly-Exploring Random Trees*) path planning algorithm to determine the laying path with minimal temperature loss. Experimental results demonstrate that this algorithm effectively preserves the object's heat retention.