DroneFL: マルチUAV視覚ターゲット追跡のための連合学習
DroneFL: Federated Learning for Multi-UAV Visual Target Tracking
複数のドローンが連合学習でターゲット追跡モデルを協調学習し、データの不均一性を軽減しつつ予測精度を向上させるフレームワークを提案。
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著者: Xiaofan Yu, Yuwei Wu, Katherine Mao, Ye Tian, Vijay Kumar, Tajana Rosing
分類: cs.RO
原文アブストラクト
Multi-robot target tracking is a fundamental problem that requires coordinated monitoring of dynamic entities in applications such as precision agriculture, environmental monitoring, disaster response, and security surveillance. While Federated Learning (FL) has the potential to enhance learning across multiple robots without centralized data aggregation, its use in multi-Unmanned Aerial Vehicle (UAV) target tracking remains largely underexplored. Key challenges include limited onboard computational resources, significant data heterogeneity in FL due to varying targets and the fields of view, and the need for tight coupling between trajectory prediction and multi-robot planning. In this paper, we introduce DroneFL, the first federated learning framework specifically designed for efficient multi-UAV target tracking. We design a lightweight local model to predict target trajectories from sensor inputs, using a frozen YOLO backbone and a shallow transformer for efficient onboard training. The updated models are periodically aggregated in the cloud for global knowledge sharing. To alleviate the data heterogeneity that hinders FL convergence, DroneFL introduces a position-invariant model architecture with altitude-based adaptive instance normalization. Finally, we fuse predictions from multiple UAVs in the cloud and generate optimal trajectories that balance target prediction accuracy and overall tracking performance. Our results show that DroneFL reduces prediction error by 6%-83% and tracking distance by 0.4%-4.6% compared to a distributed non-FL framework. In terms of efficiency, DroneFL runs in real time on a Raspberry Pi 5 and has on average just 1.56 KBps data rate to the cloud.
