解决机器人团队耗尽性能力分配与调度问题，兼顾电量与时间约束。
Modeling and Optimizing the Provisioning of Exhaustible Capabilities for Simultaneous Task Allocation and Scheduling
- 基于非线性规划优化联盟的能力供给速率，实现高效分配。
- 在复杂任务下满足电量与能力需求，且计算可处理。
- 适合需要长期协同作业的多机器人系统研究者。
将异构机器人团队部署于长时间跨度的多任务场景中，任务分配与规划面临重大计算挑战。本文提出首个能应对电池与时间约束下耗尽性能力供给问题的离线异构多机器人任务分配框架 TRAITS。我们引入基于非线性规划的能力分配模块，优化联盟的能力供给速率，获得可行且时间高效的解决方案。TRAITS通过利用能力供给速率，更准确地评估任务可行性与执行时间、总工期，并优化电池消耗——这是现有先进框架所缺乏的优势。我们在两个先进框架上对 TRAITS 进行了对比评估，结果表明其在满足复杂能力与电池需求方面具有优势，同时保持计算可处理性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。