基于机载测距的轻量级空中集群相对定位与稳定性研究
Onboard Ranging-based Relative Localization and Stability for Lightweight Aerial Swarms
AI总结：
本文提出一种融合超宽带测距与邻居共享状态的轻量级空中集群自主相对定位方法，在13架33克资源受限无人机上实现16Hz频率下小于0.2米定位误差，并发现不可观测性可通过控制动作自动恢复可观测性。
摘要：
轻量级空中集群在大型无人机无法高效运行的场景中具有潜在应用价值。轻量级空中集群的首要基础是高效的相对定位，它能够实现协同合作与碰撞避免。由于极端的资源限制，实时计算位置具有挑战性。本文提出了一种无需基础设施的轻量级空中集群自主相对定位技术，该技术融合了超宽带无线距离测量和来自邻居的共享状态信息（例如速度、偏航角速率、高度）。这是首个完全自主、微型、快速且精确的相对定位方案，在一个由13架轻量级（33克）且资源受限（168MHz MCU，192 KB内存）的飞行器组成的团队上实现。所提出的资源受限集群测距协议具有可扩展性，并且发现了一个令人意外的理论结果：不可观测性不会造成问题，因为状态漂移会导致控制动作使状态重新变得可观测。通过实验，在16Hz频率下，多达13架无人机实现了小于0.2米的位置误差。代码已开源，所提出的技术不仅适用于微型无人机，还可以直接应用于许多其他资源受限的机器人。视频和代码可在\ extnormal{\url{https://shushuai3.github.io/autonomous-swarm/}}找到。
英文摘要：
Lightweight aerial swarms have potential applications in scenarios where larger drones fail to operate efficiently. The primary foundation for lightweight aerial swarms is efficient relative localization, which enables cooperation and collision avoidance. Computing the real-time position is challenging due to extreme resource constraints. This paper presents an autonomous relative localization technique for lightweight aerial swarms without infrastructure by fusing ultra-wideband wireless distance measurements and the shared state information (e.g., velocity, yaw rate, height) from neighbors. This is the first fully autonomous, tiny, fast, and accurate relative localization scheme implemented on a team of 13 lightweight (33 grams) and resource-constrained (168MHz MCU with 192 KB memory) aerial vehicles. The proposed resource-constrained swarm ranging protocol is scalable, and a surprising theoretical result is discovered: the unobservability poses no issues because the state drift leads to control actions that make the state observable again. By experiment, less than 0.2m position error is achieved at the frequency of 16Hz for as many as 13 drones. The code is open-sourced, and the proposed technique is relevant not only for tiny drones but can be readily applied to many other resource-restricted robots. Video and code can be found at \textnormal{\url{https://shushuai3.github.io/autonomous-swarm/}}.
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。
