Robotic collectives for military and disaster response applications require coalition formation algorithms to partition robots into appropriate task teams. Collectives' missions will often incorporate tasks that require multiple high-level robot behaviors or services, which coalition formation must accommodate. The highly dynamic and unstructured application domains also necessitate that coalition formation algorithms produce near optimal solutions (i.e., >95% utility) in near real-time (i.e., <5 minutes) with very large collectives (i.e., hundreds of robots). No previous coalition formation algorithm satisfies these requirements. An initial evaluation found that traditional auction-based algorithms' runtimes are too long, even though the centralized simulator incorporated ideal conditions unlikely to occur in real-world deployments (i.e., synchronization across robots and perfect, instantaneous communication). The hedonic game-based GRAPE algorithm can produce solutions in near real-time, but cannot be applied to multiple service collectives. This manuscript integrates GRAPE and a services model, producing GRAPE-S and Pair-GRAPE-S. These algorithms and two auction baselines were evaluated using a centralized simulator with up to 1000 robots, and via the largest distributed coalition formation simulated evaluation to date, with up to 500 robots. The evaluations demonstrate that auctions transfer poorly to distributed collectives, resulting in excessive runtimes and low utility solutions. GRAPE-S satisfies the target domains' coalition formation requirements, producing near optimal solutions in near real-time, and Pair-GRAPE-S more than satisfies the domain requirements, producing optimal solutions in near real-time. GRAPE-S and Pair-GRAPE-S are the first algorithms demonstrated to support near real-time coalition formation for very large, distributed collectives with multiple services.

翻译：[translated abstract in Chinese] 针对军事和灾难响应应用中的机器人群体，需要联盟形成算法将机器人划分为合适的任务团队。群体任务通常包含需要多种高层机器人行为或服务的任务，联盟形成算法必须适应这一需求。高度动态和非结构化的应用领域还要求联盟形成算法能在近实时（<5分钟）内为超大规模群体（数百个机器人）生成近最优解（即效用>95%）。现有联盟形成算法均无法满足这些要求。初步评估发现，即使集中式模拟器采用现实部署中难以实现的理想条件（如机器人间同步、完美即时通信），传统基于拍卖的算法运行时间仍过长。基于享乐博弈的GRAPE算法虽可近实时生成解，但无法应用于多服务群体。本文整合GRAPE与服务模型，提出GRAPE-S和Pair-GRAPE-S两种算法。通过集中式模拟器（支持多达1000个机器人）和迄今最大规模的分布式联盟形成模拟评估（支持多达500个机器人），对上述算法及两个拍卖基线进行了测试。评估结果表明：拍卖算法难以迁移至分布式群体，导致过长运行时间和低效用解；GRAPE-S满足目标领域联盟形成需求，可在近实时内生成近最优解；Pair-GRAPE-S则超额满足领域需求，能在近实时内生成最优解。GRAPE-S和Pair-GRAPE-S是首个被验证支持超大规模、分布式、多服务群体近实时联盟形成的算法。