Aerial Shepherds: Enabling Hierarchical Localization in Heterogeneous MAV Swarms
Journal:
arXiv
Published Date:
Jun 10, 2025
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.
