Complex multi-objective missions require the coordination of heterogeneous robots at multiple inter-connected levels, such as coalition formation, scheduling, and motion planning. The associated challenges are exacerbated when solutions to these interconnected problems need to both maximize task performance and respect practical constraints on time and resources. In this work, we formulate a new class of spatio-temporal heterogeneous task allocation problems that consider these complexities. We contribute a novel framework, named Quality-Optimized Incremental Task Allocation Graph Search (Q-ITAGS), to solve such problems. Q-ITAGS builds upon our prior work in trait-based coordination and offers a flexible interleaved framework that i) explicitly models and optimizes the effect of collective capabilities on task performance via learnable trait-quality maps, and ii) respects both resource constraints and spatio-temporal constraints, including a user-specified time budget (i.e., maximum makespan). In addition to algorithmic contributions, we derive theoretical suboptimality bounds in terms of task performance that varies as a function of a single hyperparameter. Our detailed experiments involving a simulated emergency response task and a real-world video game dataset reveal that i) Q-ITAGS results in superior team performance compared to a state-of-the-art method, while also respecting complex spatio-temporal and resource constraints, ii) Q-ITAGS efficiently learns trait-quality maps to enable effective trade-off between task performance and resource constraints, and iii) Q-ITAGS' suboptimality bounds consistently hold in practice.

翻译：复杂多目标任务需要在多个相互关联的层级上协调异构机器人，例如联盟形成、调度和运动规划。当这些相互关联问题的解决方案需要同时最大化任务性能并遵守时间和资源的实际约束时，相关的挑战会进一步加剧。本文提出了一类考虑这些复杂性的新型时空异构任务分配问题。我们贡献了一个名为“质量优化增量任务分配图搜索”（Q-ITAGS）的新框架来解决此类问题。Q-ITAGS基于我们先前在基于特征的协调方面的工作，提供了一个灵活的交叉框架，该框架能够：i）通过可学习的特性-质量映射显式建模并优化集体能力对任务性能的影响，以及ii）同时满足资源约束和时空约束，包括用户指定的时间预算（即最大完工时间）。除算法贡献外，我们还推导了任务性能方面的理论次优性界限，该界限随单个超参数的变化而改变。涉及模拟应急响应任务和真实世界视频游戏数据集的详细实验表明：i）与最先进方法相比，Q-ITAGS在遵守复杂时空和资源约束的同时，实现了更优的团队性能；ii）Q-ITAGS能够有效学习特性-质量映射，实现任务性能与资源约束之间的有效权衡；以及iii）Q-ITAGS的次优性界限在实践中始终成立。