Automatic Robotic Assembly Sequence Planning (RASP) can significantly improve productivity and resilience in modern manufacturing along with the growing need for greater product customization. One of the main challenges in realizing such automation resides in efficiently finding solutions from a growing number of potential sequences for increasingly complex assemblies. Besides, costly feasibility checks are always required for the robotic system. To address this, we propose a holistic graphical approach including a graph representation called Assembly Graph for product assemblies and a policy architecture, Graph Assembly Processing Network, dubbed GRACE for assembly sequence generation. With GRACE, we are able to extract meaningful information from the graph input and predict assembly sequences in a step-by-step manner. In experiments, we show that our approach can predict feasible assembly sequences across product variants of aluminum profiles based on data collected in simulation of a dual-armed robotic system. We further demonstrate that our method is capable of detecting infeasible assemblies, substantially alleviating the undesirable impacts from false predictions, and hence facilitating real-world deployment soon. Code and training data are available at https://github.com/DLR-RM/GRACE.

翻译：自动机器人装配序列规划（RASP）能够在产品定制化需求日益增长的背景下，显著提升现代制造业的生产效率和适应性。实现此类自动化的主要挑战之一，在于如何从数量激增的潜在序列中为日益复杂的装配任务高效寻找可行方案。此外，机器人系统始终需要代价高昂的可行性校验。为此，我们提出一种整体性图方法，包括用于产品装配的图表示——装配图（Assembly Graph），以及用于装配序列生成的策略架构——图装配处理网络（Graph Assembly Processing Network，简称GRACE）。借助GRACE，我们能够从图输入中提取有效信息，并以逐步递进的方式预测装配序列。实验表明，基于双臂机器人系统仿真数据，我们的方法可针对铝合金型材的不同产品变体预测可行的装配序列。我们进一步证明，该方法能够检测不可行的装配方案，显著减轻错误预测带来的不利影响，从而为现实部署提供有力支持。代码与训练数据可于 https://github.com/DLR-RM/GRACE 获取。