Planning Assembly Sequence with Graph Transformer

• URL: http://arxiv.org/abs/2210.05236v2
• Date: Wed, 12 Oct 2022 15:00:34 GMT
• Title: Planning Assembly Sequence with Graph Transformer
• Authors: Lin Ma, Jiangtao Gong, Hao Xu, Hao Chen, Hao Zhao, Wenbing Huang and Guyue Zhou
• Abstract summary: Assembly sequence planning (ASP) is proven to be NP-complete thus its effective and efficient solution has been a challenge for researchers in the field. We present a graph-transformer based framework for the ASP problem which is trained and demonstrated on a self-collected ASP database.
• Score: 35.2954163574535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Assembly sequence planning (ASP) is the essential process for modern manufacturing, proven to be NP-complete thus its effective and efficient solution has been a challenge for researchers in the field. In this paper, we present a graph-transformer based framework for the ASP problem which is trained and demonstrated on a self-collected ASP database. The ASP database contains a self-collected set of LEGO models. The LEGO model is abstracted to a heterogeneous graph structure after a thorough analysis of the original structure and feature extraction. The ground truth assembly sequence is first generated by brute-force search and then adjusted manually to in line with human rational habits. Based on this self-collected ASP dataset, we propose a heterogeneous graph-transformer framework to learn the latent rules for assembly planning. We evaluated the proposed framework in a series of experiment. The results show that the similarity of the predicted and ground truth sequences can reach 0.44, a medium correlation measured by Kendall's $\tau$. Meanwhile, we compared the different effects of node features and edge features and generated a feasible and reasonable assembly sequence as a benchmark for further research. Our data set and code is available on https://github.com/AIR-DISCOVER/ICRA\_ASP.

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