Robot teams are increasingly being deployed in environments, such as manufacturing facilities and warehouses, to save cost and improve productivity. To efficiently coordinate multi-robot teams, fast, high-quality scheduling algorithms are essential to satisfy the temporal and spatial constraints imposed by dynamic task specification and part and robot availability. Traditional solutions include exact methods, which are intractable for large-scale problems, or application-specific heuristics, which require expert domain knowledge to develop. In this paper, we propose a novel heterogeneous graph attention network model, called ScheduleNet, to learn scheduling policies that overcome the limitations of conventional approaches. By introducing robot- and proximity-specific nodes into the simple temporal network encoding temporal constraints, we obtain a heterogeneous graph structure that is nonparametric in the number of tasks, robots and task resources or locations. We show that our model is end-to-end trainable via imitation learning on small-scale problems, and generalizes to large, unseen problems. Empirically, our method outperforms the existing state-of-the-art methods in a variety of testing scenarios involving both homogeneous and heterogeneous robot teams.

机器人团队越来越多地部署在制造设施和仓库等环境中，以节省成本并提高生产力。为了有效地协调多机器人团队，快速、高质量的调度算法对于满足动态任务规范以及零件和机器人可用性强加的时间和空间约束至关重要。传统的解决方案包括精确方法，这对于大规模问题来说是难以处理的，或者特定于应用程序的启发式方法，需要专家领域知识来开发。在本文中，我们提出了一种新的异构图注意力网络模型，称为 ScheduleNet，以学习克服传统方法局限性的调度策略。通过将特定于机器人和邻近度的节点引入到编码时间约束的简单时间网络中，我们获得了一个异构图结构，它在任务、机器人和任务资源或位置的数量方面是非参数的。我们表明我们的模型可以通过对小规模问题的模仿学习进行端到端的训练，并且可以推广到大型的、看不见的问题。根据经验，我们的方法在涉及同质和异质机器人团队的各种测试场景中优于现有的最先进方法。