Applications, such as military and disaster response, can benefit from robotic collectives' ability to perform multiple cooperative tasks (e.g., surveillance, damage assessments) efficiently across a large spatial area. Coalition formation algorithms can potentially facilitate collective robots' assignment to appropriate task teams; however, most coalition formation algorithms were designed for smaller multiple robot systems (i.e., 2-50 robots). Collectives' scale and domain-relevant constraints (i.e., distribution, near real-time, minimal communication) make coalition formation more challenging. This manuscript identifies the challenges inherent to designing coalition formation algorithms for very large collectives (e.g., 1000 robots). A survey of multiple robot coalition formation algorithms finds that most are unable to transfer directly to collectives, due to the identified system differences; however, auctions and hedonic games may be the most transferable. A simulation-based evaluation of three auction and hedonic game algorithms, applied to homogeneous and heterogeneous collectives, demonstrates that there are collective compositions for which no existing algorithm is viable; however, the experimental results and literature survey suggest paths forward.

翻译：在军事和灾难响应等应用场景中，机器人集群在大范围空间内高效执行多项协作任务（如监视、损伤评估）的能力具有显著价值。联盟形成算法有望实现集群机器人与相应任务团队的合理分配，然而多数现有联盟形成算法专门针对中小型多机器人系统（如2至50个机器人）设计。集群的规模特性及领域相关约束条件（包括分布式架构、近实时性要求、最小化通信需求）使得联盟形成面临更大挑战。本文揭示了为超大规模集群（如1000个机器人）设计联盟形成算法所固有的难点。通过对多机器人联盟形成算法的系统性调查发现，由于上述系统差异的存在，大多数算法无法直接迁移至集群场景，其中拍卖博弈与享乐博弈可能具有最佳可迁移性。针对三种拍卖与享乐博弈算法在同构与异构集群环境下的仿真评估表明，现有算法在某些集群组成结构下均不可行；但实验数据与文献综述共同指明了潜在的发展方向。