TransformLoc: Transforming MAVs into Mobile Localization Infrastructures in Heterogeneous Swarms

TL;DR

TransformLoc tackles the BMAV localization challenge in heterogeneous MAV swarms by turning a few AMAVs into mobile localization infrastructures for many BMAVs. It combines an error-aware joint location estimation framework, which uses estimation uncertainty to guide when AMAV observations are needed, with a proximity-driven adaptive grouping-scheduling strategy that partitions BMAVs via Voronoi regions and plans non-myopic AMAV observations. The approach is validated through real-world experiments and physical-feature-based simulations, achieving up to $68\%$ localization improvements and up to $60\%$ navigation gains over baselines, while maintaining low communication overhead. This work enables scalable, cost-effective swarm localization without reliance on pre-deployed infrastructure and has strong implications for rapid-response MAV applications.

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, turning this insight into a practical system is non-trivial due to challenges in location estimation with BMAVs' unknown and diverse localization errors and resource allocation of AMAVs given coupled influential factors. This study proposes TransformLoc, a new framework that transforms AMAVs into mobile localization infrastructures, specifically designed for low-cost and resource-constrained BMAVs. We first design an error-aware joint location estimation model to perform intermittent joint location estimation for BMAVs and then design a proximity-driven 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 TransformLoc on industrial drones and validate its performance. Results show that TransformLoc outperforms baselines including SOTA up to 68\% in localization performance, motivating up to 60\% navigation success rate improvement.

Figures (9)

• Figure 1: Introduction of heterogeneous MAV swarm. The AMAVs are resource-intensive and have a lower localization error and latency, while BMAVs are resource-limited, resulting in a high error and latency.
• Figure 2: Motivating example. (a) BMAVs have limited on-board resources, resulting in a trade-off between accuracy and latency; (b) The accuracy of observation is affected by distances and angles between MAVs.
• Figure 3: Illustration of TransformLoc framework. BMAVs estimate locations with noisy measurements. With the assistance of the uncertainty-aided inference method, AMAV generates discontinuous observations for BMAVs to perform intermittent joint location estimation. Subsequently, TransformLoc allocates resources of AMAVs by adaptive grouping and scheduling, which adaptively groups MAVs at first, and then schedules AMAVs in a non-myopic manner.
• Figure 4: The error-aware joint location estimation model. (a) AMAV determines the BMAV with a higher error under the assistance of the uncertainty-aided inference method, and (b) generates observations for correction.
• Figure 5: The proximity-driven adaptive grouping-scheduling strategy. This strategy groups MAVs based on the Voronoi diagram to decouple the resource allocation problem. Then, it constructs search trees for each AMAV by involving several steps lookahead about BMAVs, resulting in $\delta$-step paths for AMAVs. AMAV is scheduled in a non-myopic way to assist BMAVs with significant errors in an optimal distance and angle. $x_{j, 0}$ is state of $A_j$, $\Sigma_{j, 0}$ is covariance of assigned BMAVs' estimation maintained by $A_j$, and $\hat{y}_0$ is BMAVs' estimation location.