Land & Localize：一种无需基础设施且可扩展的基于UWB定位的纳米无人机蜂群
Land & Localize: An Infrastructure-free and Scalable Nano-Drones Swarm with UWB-based Localization
AI总结：
本研究针对10厘米级纳米无人机机载功率受限问题，提出无需固定基础设施的UWB定位蜂群方案，通过部分无人机充当可自动部署的动态锚点实现灵活定位，在多场景下验证了其精度、可扩展性与低功耗优势。
摘要：
相对定位是任何机器人群体的核心功能模块。我们针对机身尺寸为10厘米级的纳米无人机群解决这一问题，这类无人机通用性极强，但机载功率预算受到严格限制。现有主流方案利用超宽带（Ultra-WideBand, UWB）技术，实现纳米无人机同伴与由多个UWB锚点组成的固定基础设施之间的距离测量。为此，我们提出一种基于UWB、无需基础设施的纳米无人机蜂群方案，其中机群中的部分无人机充当动态锚点，即锚点无人机（anchor-drones, ADs），具备自动部署和降落能力。通过调整ADs的位置约束，我们开发了三种备选方案，在灵活性与定位精度之间实现不同的权衡。针对4架飞行任务无人机（mission-drones, MDs）的实地测试结果显示，定位均方根误差（RMSE）最高在15.3厘米至27.8厘米之间。当MDs数量从4架扩展到8架时，RMSE仅有小幅上升，即最大增幅不超过10厘米。MDs的UWB模块功耗为342毫瓦。最终，与采用固定基础设施的商用方案相比，我们的无基础设施系统可在任意地点快速部署：4架ADs完成自定位仅需5.7秒；在8架MDs的最具挑战性场景下，定位RMSE最高可达12.3%。
英文摘要：
Relative localization is a crucial functional block of any robotic swarm. We address it in a fleet of nano-drones characterized by a 10 cm-scale form factor, which makes them highly versatile but also strictly limited in their onboard power envelope. State-of-the-Art solutions leverage Ultra-WideBand (UWB) technology, allowing distance range measurements between peer nano-drones and a stationary infrastructure of multiple UWB anchors. Therefore, we propose an UWB-based infrastructure-free nano-drones swarm, where part of the fleet acts as dynamic anchors, i.e., anchor-drones (ADs), capable of automatic deployment and landing. By varying the Ads' position constraint, we develop three alternative solutions with different trade-offs between flexibility and localization accuracy. In-field results, with four flying mission-drones (MDs), show a localization root mean square error (RMSE) spanning from 15.3 cm to 27.8 cm, at most. Scaling the number of MDs from 4 to 8, the RMSE marginally increases, i.e., less than 10 cm at most. The power consumption of the MDs' UWB module amounts to 342 mW. Ultimately, compared to a fixed-infrastructure commercial solution, our infrastructure-free system can be deployed anywhere and rapidly by taking 5.7 s to self-localize 4 ADs with a localization RMSE of up to 12.3% in the most challenging case with 8 MDs.
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。
