A Deep Learning Modeling Method for the Identification of Robot Curtain Wall Assembly State

Abstract

The precise identification of the robot curtain wall assembly state is crucial for improving construction efficiency. Traditional methods remain sensitive to noise in high-dimensional sensor data and require extensive datasets, resulting in limited generalization. A method for identifying the robot curtain wall assembly state based on the ensemble deep learning is proposed. This method first trains a set of diverse base models, and then integrates their predictions by training a meta-model. Each base model is composed of a Convolutional Neural Network and a Gated Recurrent Unit, and this serial structure enhances the model's generalization performance. To retain more useful information and reduce irrelevant interference, the sample data is divided into multiple sample blocks by fuzzy DBSCAN clustering method, and these sample blocks are used as the base model's inputs. A self-attention mechanism and a Multilayer Perceptron are introduced to form a meta model to improve assembly state recognition. The experimental results show that the proposed model has improved the accuracy by 4.5%. Compared with four high-precision models, the proposed model achieves the highest accuracy in 5 out of 12 UEA public datasets, with an average rank of 2.417 and an average accuracy of 0.832, which demonstrates strong generality and robustness.