动态调整任务包大小，实现多机器人实时高效任务分配。
Varying Bundle Size Reactive Multi-Task Assignment using Selective Cost Estimation for Multi-Agent Systems
- 分两阶段生成任务包：先用低精度启发式快速筛选，再对优质候选进行高精度路径规划。
- 在多个仿真环境中显著提升反应式拍卖任务分配的性能和可扩展性。
- 适合需要实时响应、保护隐私和复杂环境的多智能体系统部署。
本文提出一种可扩展的多机器人任务分配框架，适用于执行成本估算计算开销大的复杂环境。尽管基于组合拍卖的方法能提供可靠解，但任务包生成的指数级复杂度通常使其难以用于实时响应应用，尤其是在需要精确路径规划验证成本时。为此，我们设计了一种分布式、两阶段的多保真度任务包生成方法：各智能体利用基于低保真度启发式（如欧氏距离）的局部搜索树快速探索任务包空间，仅对最有前景的候选任务包采用高保真度路径规划，并以最佳优先方式处理。这些优化后的投标提交至中央协调器，由其求解集合打包问题以确保全局可行性并最大化总效用。多环境仿真结果表明，该框架显著提升了反应式拍卖式任务分配的性能。整体而言，所提框架能够在不暴露智能体状态和内部成本估计模型的前提下，支持多种场景下的动态任务包大小反应式任务分配。
原文摘要 · Abstract (English)
This paper presents a scalable framework for multi-robot task allocation in complex environments where estimating task execution costs is computationally expensive. While combinatorial auction-based approaches offer reliable solutions, the exponential complexity of bundle generation typically renders them intractable for real-time reactive applications, particularly when accurate path planning is required for cost validation. We address this through a distributed, two-stage multi-fidelity bundle generation approach. Agents utilize a local search tree guided by a low-fidelity heuristic (such as euclidean distance) to rapidly explore the bundle space, applying high-fidelity path planning only to the most promising candidates in a best-first manner. These refined bids are then submitted to a central coordinator that solves a set packing problem to ensure global feasibility and maximize the overall utility. Simulation results in multiple environments demonstrate that the framework is able to improve the performance of reactive auction-based task allocation. Overall, the presented framework is shown to enable reactive task allocation with dynamic bundle sizes in multiple settings without exposing the agents' state and internal cost estimation models.
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