In this paper, we introduce a novel adaptation of the Raft consensus algorithm for achieving emergent formation control in multi-agent systems with a single integrator dynamics. This strategy, dubbed "Rafting," enables robust cooperation between distributed nodes, thereby facilitating the achievement of desired geometric configurations. Our framework takes advantage of the Raft algorithm's inherent fault tolerance and strong consistency guarantees to extend its applicability to distributed formation control tasks. Following the introduction of a decentralized mechanism for aggregating agent states, a synchronization protocol for information exchange and consensus formation is proposed. The Raft consensus algorithm combines leader election, log replication, and state machine application to steer agents toward a common, collaborative goal. A series of detailed simulations validate the efficacy and robustness of our method under various conditions, including partial network failures and disturbances. The outcomes demonstrate the algorithm's potential and open up new possibilities in swarm robotics, autonomous transportation, and distributed computation. The implementation of the algorithms presented in this paper is available at https://github.com/abbas-tari/raft.git.

翻译：本文提出了一种对Raft共识算法的创新性改进，用于实现具有单积分器动力学的多智能体系统中的涌现编队控制。该策略被称为"Rafting"（基于Raft的编队机制），能够使分布式节点间实现鲁棒协同，从而达成预期的几何构型。我们的框架利用Raft算法固有的容错性与强一致性保证，将其应用范围扩展至分布式编队控制任务。在提出去中心化的智能体状态聚合机制后，本文设计了一种用于信息交换与共识达成的同步协议。Raft共识算法通过融合领导者选举、日志复制与状态机应用，引导智能体向共同协作目标趋近。一系列详尽的仿真实验验证了该方法在部分网络失效及扰动等多种条件下的有效性与鲁棒性。研究结果表明该算法具备巨大潜力，为集群机器人、自主运输及分布式计算领域开辟了新可能。本文所述算法的实现代码已开源至https://github.com/abbas-tari/raft.git。