Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning
This addresses the problem of automating assembly planning in manufacturing to improve productivity and customization, though it appears incremental as it builds on existing graph-based methods for a specific domain.
The paper tackles the challenge of efficiently generating feasible robotic assembly sequences for complex products by proposing a graph representation and a policy network called GRACE, which predicts sequences step-by-step and detects infeasible assemblies, reducing false predictions.
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.