ロボット間のコラボレーションを成功させる新しい方法を開発(UMass Amherst Researchers Create New Method for Orchestrating Successful Collaboration Among Robots)

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2024-06-17 マサチューセッツ大学アマースト校

マサチューセッツ大学アマースト校の新研究は、ロボットが自律的にチームを組み、仲間を待つようにプログラムすると、タスクの完了が速くなることを示しました。この研究はIEEE国際ロボット自動化会議2024で優秀論文賞の最終候補に選ばれました。研究チームは、学習ベースのスケジューリング手法「LVWS」を開発し、コンピュータシミュレーションで他の方法と比較しました。LVWSは理想的な解決策に対して0.8%の差で、他の方法よりも優れていました。ロボットが仲間を待つことで、全体の効率が向上することが確認され、特に大規模な産業環境での多ロボットシステムの進展が期待されます。

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異種多ロボット協調スケジューリングにおける動的サブチーム化と自発的待機の学習 Learning for Dynamic Subteaming and Voluntary Waiting in Heterogeneous Multi-Robot Collaborative Scheduling

Williard Joshua Jose and Hao Zhang
IEEE International Conference on Robotics and Automation (ICRA) 2024

ロボット間のコラボレーションを成功させる新しい方法を開発(UMass Amherst Researchers Create New Method for Orchestrating Successful Collaboration Among Robots)

Abstract

Coordinating heterogeneous robots is essential for autonomous multi-robot teaming. To execute a set of dependent tasks as quickly as possible, and to complete tasks that cannot be addressed by individual robots, it is necessary to form subteams that can collaboratively finish the tasks. It is also advantageous for robots to wait for teammates and tasks to become available in order to form better subteams or reduce the overall completion time.

To enable both abilities, we introduce a new graph learning approach that formulates heterogeneous collaborative scheduling as a bipartite matching problem that maximizes a reward matrix learned via imitation learning. We design a novel graph attention transformer network (GATN) that represents the problem of collaborative scheduling as a bipartite graph, and integrates both local and global graph information to estimate the reward matrix using graph attention networks and transformers.

By relaxing the constraint of one-to-one correspondence in bipartite matching, our approach allows multiple robots to address the same task as a subteam. Our approach also enables voluntary waiting by introducing an idle task that the robots can select to wait. Experimental results have shown that our approach well addresses heterogeneous collaborative scheduling with dynamic subteam formation and voluntary waiting, and outperforms the previous and baseline methods.

0109ロボット
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