Q-ITAGS：基于时间预算的质量优化时空异构任务分配
Q-ITAGS: Quality-Optimized Spatio-Temporal Heterogeneous Task Allocation with a Time Budget
AI总结：
针对复杂多目标任务中的异构机器人协调问题，提出Q-ITAGS框架，通过可学习的特征-质量图优化任务性能，并在满足时空与资源约束下推导理论次优性界限，实验证明其优于现有方法。
摘要：
复杂的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，例如联盟组建、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满足时间和资源的实际约束时，相关挑战会变得更加严峻。在这项工作中，我们提出了一类新的时空异构任务分配问题，对这些复杂性进行了形式化描述。随后，我们提出了一个名为Quality-Optimized Incremental Task Allocation Graph Search (Q-ITAGS)的新框架来解决此类问题。Q-ITAGS提供了一个灵活的交错框架，它i)通过可学习的特征-质量图显式建模并优化集体能力对任务性能的影响，以及ii)同时尊重资源和时空约束，包括用户指定的时间预算（即最大完工时间）。除了算法贡献外，我们还推导了任务性能方面的理论次优性界限，该界限作为单一超参数的函数而变化。涉及模拟紧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)与最先进的方法相比，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高效学习特征-质量图，从而在任务性能和资源约束之间实现有效权衡；iii)Q-ITAGS的次优性界限在实践中始终成立。
英文摘要：
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 simultaneously maximize task performance and respect practical constraints on time and resources. In this work, we formulate a new class of spatiotemporal heterogeneous task allocation problems that formalize these complexities. We then contribute a novel framework, named Quality-Optimized Incremental Task Allocation Graph Search (Q-ITAGS), to solve such problems. Q-ITAGS offers a flexible interleaved framework that i) explicitly models and optimizes the effect of the collective capabilities on task performance via learnable trait-quality maps, and ii) respects both resource and spatiotemporal 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. 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 spatiotemporal 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
