论文标题
使用图形变压器计划组装序列
Planning Assembly Sequence with Graph Transformer
论文作者
论文摘要
组装序列规划（ASP）是现代制造业的重要过程，被证明是NP完整的，因此其有效而有效的解决方案一直是该领域研究人员的挑战。在本文中，我们为ASP问题提供了一个基于图形转换器的框架，该框架在自我收集的ASP数据库上进行了训练和演示。 ASP数据库包含一组自我收集的乐高模型。在对原始结构和特征提取的彻底分析后，将乐高模型抽象成异质图结构。地面真理组装顺序首先是通过蛮力搜索产生的，然后根据人类的理性习惯进行手动调整。基于此自行收集的ASP数据集，我们提出了一个异质的图形转换器框架，以了解用于组装计划的潜在规则。我们在一系列实验中评估了所提出的框架。结果表明，预测和地面真实序列的相似性可以达到0.44，这是肯德尔$τ$测量的介质相关性。同时，我们比较了节点特征和边缘特征的不同效果，并生成了可行且合理的组装序列，作为进一步研究的基准。我们的数据集和代码可在https://github.com/air-discover/icra \ _asp上找到。
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 $τ$. 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.
