无人机集群无需定位锚点，仍能保持稳定编队。
Swarming Without an Anchor (SWA): Robot Swarms Adapt Better to Localization Dropouts Then a Single Robot
- 仅用相对信息实现集群自主稳定，不依赖外部定位信号。
- 在定位信号中断时，仍能保持速度一致和编队凝聚。
- 适合高可靠性要求的无人机协同任务，如搜救、巡检。
本文提出一种无需定位锚点的无人机集群状态估计方法（SWA），适用于遭遇自身定位中断的多架无人飞行器。通过融合分布式状态估计、鲁棒互感知与机载传感器数据，系统在定位信号间歇失效时仍能保持精确的状态感知。利用相对信息估算无人机横向状态，可唯一确定其相对于局部集群的位置关系。该方法实现了速度一致性，本质上是双积分器同步问题。除整个集群均匀平移漂移外，所有扰动和性能退化均被抑制，为提升多无人机系统的可靠性与韧性提供了新可能。仿真与实测验证了该方法在定位不可靠或缺失条件下维持集群凝聚力的有效性。
原文摘要 · Abstract (English)
In this paper, we present the Swarming Without an Anchor (SWA) approach to state estimation in swarms of Unmanned Aerial Vehicles (UAVs) experiencing ego-localization dropout, where individual agents are laterally stabilized using relative information only. We propose to fuse decentralized state estimation with robust mutual perception and onboard sensor data to maintain accurate state awareness despite intermittent localization failures. Thus, the relative information used to estimate the lateral state of UAVs enables the identification of the unambiguous state of UAVs with respect to the local constellation. The resulting behavior reaches velocity consensus, as this task can be referred to as the double integrator synchronization problem. All disturbances and performance degradations except a uniform translation drift of the swarm as a whole is attenuated which is enabling new opportunities in using tight cooperation for increasing reliability and resilience of multi-UAV systems. Simulations and real-world experiments validate the effectiveness of our approach, demonstrating its capability to sustain cohesive swarm behavior in challenging conditions of unreliable or unavailable primary localization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。