FLIP：通过点云配准实现大规模分布式集群的实时弹性编队规划
FLIP: Real-Time and Resilient Formation Planning for Large-Scale DIstributed Swarms via Point Cloud Registration
AI总结：
提出将最优编队位置序列计算转化为时空点云配准问题，利用带离群点剔除的PCR方法实现大规模分布式集群的弹性、高效轨迹规划。
摘要：
传统的大规模编队规划要么过度简化编队表示导致性能不佳，要么采用完全协作关系导致计算负载过大。为了实现高性能和大规模编队规划，我们将最优编队位置序列（OFPS）计算问题转化为时空点云配准（PCR）问题。每个智能体通过分布式计算自身当前位置与所有其他智能体期望编队位置之间的匹配结果来获得OFPS。然后每个智能体利用OFPS优化协作编队轨迹。我们利用带离群点剔除的PCR方法快速执行大规模编队位置配准。这可以防止次优轨迹和故障智能体通过协作网络传播并影响更多智能体。因此，我们统一实现了大规模集群的弹性、高效和分布式轨迹规划。通过120架无人机编队的大规模仿真以及与最先进（SOTA）方法的严格基准测试，证明了所提方法的有效性和优越性。
英文摘要：
Traditional large-scale formation planning either oversimplify the formation representation which leads to poor performance, or they employ complete collaborative relationships, which results in excessive computational load. To achieve high-performance and large-scale formation planning, we transform the Optimal Formation Position Sequence \cite{c1} (OFPS) calculation problem into a spatiotemporal Point Cloud Registration (PCR) problem. Each agent derives its OFPS by distributively computing the matching result between current positions and the desired formation positions of all other agents. Then each agent optimizes the cooperative formation trajectory by using OFPS. We leverage the PCR method with outlier rejection to rapidly perform large-scale formation position registration. This prevents suboptimal trajectories and failed agents from propagating through the cooperative network and affecting more agents. Consequently, we uniformly achieve resilient, efficient, and distributed trajectory planning for large-scale swarms. The effectiveness and the superiority of the proposed method are demonstrated through large-scale simulations of 120-drone formation, and rigorous benchmarking against state-of-the-art (SOTA) methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
