Collaborative Scheduling with Adaptation to Failure for Heterogeneous Robot Teams
Collaborative Scheduling with Adaptation to Failure for Heterogeneous Robot Teams
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DOI：
10.1109/icra48891.2023.10161502
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Peng Gao;S. Siva;Anthony Micciche;Haotian Zhang
中科院分区：
文献类型：
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作者：
Peng Gao;S. Siva;Anthony Micciche;Haotian Zhang
协作调度是异构机器人团队协作完成复杂任务的基本能力，例如在多机器人装配应用中。为了实现协同调度，需要解决两个关键问题，包括将任务分配给异构机器人以及适应机器人故障以保证所有任务的完成。在本文中，我们介绍了一种新颖的方法，该方法将深度二分图匹配和模仿学习相结合，让异构机器人能够作为一个团队完成复杂的任务。具体来说，我们使用图注意力网络来表示任务的属性和关系。然后，我们将具有故障适应的协作调度制定为一种新的基于深度学习的二分图匹配问题，该问题通过模仿学习策略，以根据潜在任务调度的奖励来确定任务调度。在正常执行期间，我们的方法生成机器人任务对作为潜在的分配。当机器人发生故障时，我们的方法不仅可以识别单个机器人，还可以识别子团队来替换发生故障的机器人。我们进行了大量的实验来评估我们在机器人故障协作调度场景中的方法。实验结果表明，我们的方法在机器人故障适应的协作调度方面取得了有前途的、可推广的和可扩展的结果。
Collaborative scheduling is an essential ability for a team of heterogeneous robots to collaboratively complete complex tasks, e.g., in a multi-robot assembly application. To enable collaborative scheduling, two key problems should be addressed, including allocating tasks to heterogeneous robots and adapting to robot failures in order to guarantee the completion of all tasks. In this paper, we introduce a novel approach that integrates deep bipartite graph matching and imitation learning for heterogeneous robots to complete complex tasks as a team. Specifically, we use a graph attention network to represent attributes and relationships of the tasks. Then, we formulate collaborative scheduling with failure adaptation as a new deep learning-based bipartite graph matching problem, which learns a policy by imitation to determine task scheduling based on the reward of potential task schedules. During normal execution, our approach generates robot-task pairs as potential allocations. When a robot fails, our approach identifies not only individual robots but also subteams to replace the failed robot. We conduct extensive experiments to evaluate our approach in the scenarios of collaborative scheduling with robot failures. Experimental results show that our approach achieves promising, generalizable and scalable results on collaborative scheduling with robot failure adaptation.