Matan Atad*,2 , Jianxiang Feng*,1,2 , Ismael Rodríguez1,2 , Maximilian Durner1,2 and Rudolph Triebel1,2
⋆ Equal Contribution.
1: Institute of Robotics and Mechatronics, German Aerospace Center (DLR)
2: Department of Informatics, Technical University of Munich
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 the 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.
@inproceedings{atad2023efficient,
title={Efficient and feasible robotic assembly sequence planning via graph representation learning},
author={Atad, Matan and Feng, Jianxiang and Rodr{\'\i}guez, Ismael and Durner, Maximilian and Triebel, Rudolph},
booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={8262--8269},
year={2023},
organization={IEEE}
}