Deploying heterogeneous robot teams to accomplish multiple tasks over extended time horizons presents significant computational challenges for task allocation and planning. In this paper, we present a comprehensive, time-extended, offline heterogeneous multi-robot task allocation framework, TRAITS, which we believe to be the first that can cope with the provisioning of exhaustible traits under battery and temporal constraints. Specifically, we introduce a nonlinear programming-based trait distribution module that can optimize the trait-provisioning rate of coalitions to yield feasible and time-efficient solutions. TRAITS provides a more accurate feasibility assessment and estimation of task execution times and makespan by leveraging trait-provisioning rates while optimizing battery consumption -- an advantage that state-of-the-art frameworks lack. We evaluate TRAITS against two state-of-the-art frameworks, with results demonstrating its advantage in satisfying complex trait and battery requirements while remaining computationally tractable.

翻译：在长时间跨度内部署异构机器人团队以完成多项任务，对任务分配与规划提出了显著的计算挑战。本文提出了一种全面的、时间扩展的、离线的异构多机器人任务分配框架TRAITS，据我们所知，这是首个能够在电池和时间约束下处理可耗尽能力供应的框架。具体而言，我们引入了一个基于非线性规划的能力分配模块，该模块能够优化联盟的能力供应速率，从而产生可行且时间高效的解决方案。TRAITS通过利用能力供应速率，同时优化电池消耗，提供了更准确的任务执行时间和总完工时间的可行性评估与估计——这是现有先进框架所不具备的优势。我们将TRAITS与两个先进的框架进行了评估比较，结果表明其在满足复杂能力与电池需求方面具有优势，同时保持了计算上的可处理性。