Effective deployment of multi-robot teams requires solving several interdependent problems at varying levels of abstraction. Specifically, heterogeneous multi-robot systems must answer four important questions: what (task planning), how (motion planning), who (task allocation), and when (scheduling). Although there are rich bodies of work dedicated to various combinations of these questions, a fully integrated treatment of all four questions lies beyond the scope of the current literature, which lacks even a formal description of the complete problem. In this article, we address this absence, first by formalizing this class of multi-robot problems under the banner Simultaneous Task Allocation and Planning with Spatiotemporal Constraints (STAP-STC), and then by proposing a solution that we call Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling (GRSTAPS). GRSTAPS interleaves task planning, task allocation, scheduling, and motion planning, performing a multi-layer search while effectively sharing information among system modules. In addition to providing a unified solution to STAP-STC problems, GRSTAPS includes individual innovations both in task planning and task allocation. At the task planning level, our interleaved approach allows the planner to abstract away which agents will perform a task using an approach that we refer to as agent-agnostic planning. At the task allocation level, we contribute a search-based algorithm that can simultaneously satisfy planning constraints and task requirements while optimizing the associated schedule. We demonstrate the efficacy of GRSTAPS using detailed ablative and comparative experiments in a simulated emergency-response domain. Results of these experiments conclusively demonstrate that GRSTAPS outperforms both ablative baselines and state-of-the-art temporal planners in terms of computation time, solution quality, and problem coverage.

多机器人团队的有效部署需要解决不同抽象级别的几个相互依赖的问题。具体来说，异构多机器人系统必须回答四个重要问题：什么（任务规划）、如何（运动规划）、谁（任务分配）和何时（调度）。尽管有丰富的工作致力于这些问题的各种组合，但对所有四个问题的完全整合处理超出了当前文献的范围，目前文献甚至缺乏对完整问题的正式描述。在本文中，我们首先通过将此类多机器人问题形式化为旗帜下来解决这种缺失具有时空约束的同时任务分配和规划 (STAP-STC)，然后通过提出一种我们称之为图形递归同时任务分配、规划和调度(GRSTAPS)的解决方案。GRSTAPS 将任务规划、任务分配、调度和运动规划交织在一起，在执行多层搜索的同时有效地在系统模块之间共享信息。除了为 STAP-STC 问题提供统一的解决方案外，GRSTAPS 还包括在任务规划和任务分配方面的个人创新。在任务规划级别，我们的交错方法允许规划器使用我们称为代理不可知规划的方法抽象出哪些代理将执行任务. 在任务分配层面，我们提供了一种基于搜索的算法，该算法可以同时满足规划约束和任务要求，同时优化相关的日程安排。我们在模拟应急响应领域使用详细的消融和比较实验证明了 GRSTAPS 的功效。这些实验的结果最终证明 GRSTAPS 在计算时间、解决方案质量和问题覆盖率方面都优于消融基线和最先进的时间规划器。