Aerial Shepherds: Enabling Hierarchical Localization in Heterogeneous MAV Swarms

2025-06-10

AI Summary

In heterogeneous micro-UAV swarms, low-cost base micro-air vehicles (BMAVs) face significant challenges in achieving high-accuracy, low-latency, and low-overhead real-time localization under GPS- and infrastructure-denied environments. Method: This paper proposes a hierarchical collaborative localization framework leveraging high-performance assistant micro-air vehicles (AMAVs) as dynamic, mobile positioning infrastructure. We introduce an error-aware joint localization model to decouple error propagation across heterogeneous nodes; design a similarity-guided graph neural network–based grouping mechanism; and develop an online optimization–driven adaptive AMAV scheduling strategy to enable intermittent, resource-constrained joint estimation among BMAVs. Contribution/Results: The proposed embedded lightweight fusion framework, validated on industrial-grade hardware, achieves up to 68% improvement in localization accuracy and a 60% increase in navigation success rate, significantly enhancing robustness and real-time performance of large-scale heterogeneous UAV swarms.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Enabling low-cost real-time localization for lightweight MAVs
• Addressing diverse unknown errors in MAV location estimation
• Optimizing resource allocation in heterogeneous MAV swarms

Innovation

• Transforms AMAVs into mobile localization infrastructures
• Error-aware joint location estimation model for BMAVs
• Similarity-instructed adaptive grouping-scheduling strategy