Title: Aerial Shepherds: Enabling Hierarchical Localization in Heterogeneous MAV Swarms Show Chinese title

Title: 空中牧羊人：实现异构微型飞行器集群的分层定位

Abstract: A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-effective basic MAVs (BMAVs), offering opportunities in diverse fields. Accurate and real-time localization is crucial for MAV swarms, but current practices lack a low-cost, high-precision, and real-time solution, especially for lightweight BMAVs. We find an opportunity to accomplish the task by transforming AMAVs into mobile localization infrastructures for BMAVs. However, translating this insight into a practical system is challenging due to issues in estimating locations with diverse and unknown localization errors of BMAVs, and allocating resources of AMAVs considering interconnected influential factors. This work introduces TransformLoc, a new framework that transforms AMAVs into mobile localization infrastructures, specifically designed for low-cost and resource-constrained BMAVs. We design an error-aware joint location estimation model to perform intermittent joint estimation for BMAVs and introduce a similarity-instructed adaptive grouping-scheduling strategy to allocate resources of AMAVs dynamically. TransformLoc achieves a collaborative, adaptive, and cost-effective localization system suitable for large-scale heterogeneous MAV swarms. We implement and validate TransformLoc on industrial drones. Results show it outperforms all baselines by up to 68\% in localization performance, improving navigation success rates by 60\%. Extensive robustness and ablation experiments further highlight the superiority of its design. Show Chinese abstract

Abstract: 异构微型空中飞行器（MAV）集群由资源密集但昂贵的高级MAV（AMAV）和资源有限但具有成本效益的基本MAV（BMAV）组成，在多个领域提供了机遇。 对于MAV集群而言，精确且实时的定位至关重要，但目前的实践缺乏一种低成本、高精度且实时的解决方案，尤其是在轻量级BMAV上。 我们发现通过将AMAV转变为BMAV的移动定位基础设施来完成任务的机会。 然而，由于估计带有不同且未知的定位误差的BMAV位置以及考虑相互影响因素而分配AMAV资源的问题，将其转化为实用系统具有挑战性。 本研究引入了TransformLoc，这是一种新框架，专门设计用于低成本且资源受限的BMAV，将AMAV转变为移动定位基础设施。 我们设计了一个误差感知联合定位估计模型，以对BMAV进行间歇性联合估计，并提出了一种基于相似性的自适应分组调度策略，以动态分配AMAV资源。 TransformLoc实现了适合大规模异构MAV集群的协作、自适应且具成本效益的定位系统。 我们在工业无人机上实现并验证了TransformLoc。 结果显示，与所有基线相比，其定位性能提高了多达68%，导航成功率提高了60%。 广泛的鲁棒性和消融实验进一步突显了其设计的优势。