Q-ITAGS: Quality-Optimized Spatio-Temporal Heterogeneous Task Allocation with a Time Budget
CoRR(2024)
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 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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要