发表机构
The University of Texas at Rio Grande Valley; University of California, Irvine（德克萨斯大学里奥格兰德河谷分校; 加州大学欧文分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对CBBA在多机器人分布式任务分配中空间分布环境下的次优问题，提出GACA算法，通过分组投标机制提升最优性，在多场景测试中优于CBBA且具备良好可扩展性。
AI 中文摘要
分布式多机器人任务分配（MRTA）对于可扩展且鲁棒的自主系统至关重要。基于共识的捆绑算法（CBBA）是广泛采用的分布式基线算法，但它的个体任务级投标与最小化团队总行驶距离的最小和目标匹配度较差，导致在空间分布环境中出现次优分配。本文提出分组拍卖共识算法（GACA），该分布式MRTA框架采用CBBA的两阶段拍卖-共识架构，同时从根本上重新设计其投标机制，以对空间邻近的任务组进行推理。最近邻预处理步骤在分配前将任务划分为空间连贯的组，智能体随后迭代提出结构化的组级动作：认领未分配的组、获取部分组或争夺其他智能体持有的组，竞争动作通过共识阶段解决。在MT-SR-IA问题类中，以混合整数线性规划作为最优性参考，在四种群体规模和4000个测试环境中评估GACA，结果显示GACA的中位数最优性百分比约为97%，而CBBA为81%-84%，且GACA在相等或更少的迭代中收敛。对3280个额外问题实例的可扩展性评估（涵盖5至20个智能体、10至50个任务的群体规模）证实，这些优势在广泛的问题配置中均稳健存在。
英文摘要
Decentralized multi-robot task allocation (MRTA) is essential for scalable and resilient autonomous systems. The Consensus-Based Bundle Algorithm (CBBA) is a widely adopted decentralized baseline. However, its individual task-level bidding is poorly aligned with the min-sum objective of minimizing total team travel distance, leading to suboptimal allocations in spatially distributed environments. This paper introduces the Grouping Auction-Consensus Algorithm (GACA). This decentralized MRTA framework adopts the two-phase auction-consensus architecture of CBBA while fundamentally redesigning its bidding mechanism to reason over groups of spatially proximate tasks. A nearest-neighbor preprocessing step partitions tasks into spatially coherent groups before allocation. Agents then iteratively propose structured group-level actions: claiming unassigned groups, acquiring partial groups, or contesting groups held by other agents. Competing actions are resolved through a consensus phase. Operating in the MT-SR-IA problem class, GACA is evaluated against CBBA using a Mixed-Integer Linear Program as the ground-truth optimality reference. Across four swarm sizes and 4,000 test worlds, GACA achieves a median percent optimality of approximately 97% compared to 81--84% for CBBA, while converging in equal or fewer iterations. A scalability evaluation over 3,280 additional problem instances spanning swarm sizes of 5 to 20 agents and task counts of 10 to 50 confirms that these gains generalize robustly across a wide range of problem configurations.
