TransformLoc: Transforming MAVs into Mobile Localization Infrastructures in Heterogeneous Swarms
<|reference_start|>TransformLoc: Transforming MAVs into Mobile Localization Infrastructures in Heterogeneous Swarms: 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.<|reference_end|>
@article{wang2024transformloc:,
    title={TransformLoc: Transforming MAVs into Mobile Localization Infrastructures
  in Heterogeneous Swarms},
    author={Haoyang Wang, Jingao Xu, Chenyu Zhao, Zihong Lu, Yuhan Cheng, Xuecheng
  Chen, Xiao-Ping Zhang, Yunhao Liu, Xinlei Chen},
    journal={IEEE INFOCOM 2024 - IEEE Conference on Computer Communications},
    year={2024},
    doi={10.1109/INFOCOM52122.2024.10621375},
    archivePrefix={arXiv},
    eprint={2403.08815},
    primaryClass={cs.NI cs.RO}
}