Related papers: Graph Neural Network-Based Predictive Modeling for Robotic Plaster Printing

Graph Neural Network-Based Predictive Modeling for Robotic Plaster Printing

URL: http://arxiv.org/abs/2503.24130v1
Date: Mon, 31 Mar 2025 14:15:00 GMT
Title: Graph Neural Network-Based Predictive Modeling for Robotic Plaster Printing
Authors: Diego Machain Rivera, Selen Ercan Jenny, Ping Hsun Tsai, Ena Lloret-Fritschi, Luis Salamanca, Fernando Perez-Cruz, Konstantinos E. Tatsis,
Abstract summary: This work proposes a Graph Neural Network (GNN) modeling approach to predict the resulting surface from a particle based fabrication process.<n>The GNN model consists of an encoder-processor-decoder architecture and is trained using data from laboratory tests.<n>The performance of the proposed model is assessed in terms of the prediction error against unseen ground truth data.
Score: 37.80005110808392
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: This work proposes a Graph Neural Network (GNN) modeling approach to predict the resulting surface from a particle based fabrication process. The latter consists of spray-based printing of cementitious plaster on a wall and is facilitated with the use of a robotic arm. The predictions are computed using the robotic arm trajectory features, such as position, velocity and direction, as well as the printing process parameters. The proposed approach, based on a particle representation of the wall domain and the end effector, allows for the adoption of a graph-based solution. The GNN model consists of an encoder-processor-decoder architecture and is trained using data from laboratory tests, while the hyperparameters are optimized by means of a Bayesian scheme. The aim of this model is to act as a simulator of the printing process, and ultimately used for the generation of the robotic arm trajectory and the optimization of the printing parameters, towards the materialization of an autonomous plastering process. The performance of the proposed model is assessed in terms of the prediction error against unseen ground truth data, which shows its generality in varied scenarios, as well as in comparison with the performance of an existing benchmark model. The results demonstrate a significant improvement over the benchmark model, with notably better performance and enhanced error scaling across prediction steps.

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