イベントストリームと深層強化学習によるUAVのエンドツーエンド追跡
Leveraging Event Streams with Deep Reinforcement Learning for End-to-End UAV Tracking
イベントカメラの生データを深層強化学習で直接UAVの制御指令に変換し、高速移動や照明変化に強いエンドツーエンドの追跡手法を提案した。
著者: Ala Souissi, Hajer Fradi, Panagiotis Papadakis
分類: cs.RO, cs.AI, cs.NE
原文アブストラクト
In this paper, we present our proposed approach for active tracking to increase the autonomy of Unmanned Aerial Vehicles (UAVs) using event cameras, low-energy imaging sensors that offer significant advantages in speed and dynamic range. The proposed tracking controller is designed to respond to visual feedback from the mounted event sensor, adjusting the drone movements to follow the target. To leverage the full motion capabilities of a quadrotor and the unique properties of event sensors, we propose an end-to-end deep-reinforcement learning (DRL) framework that maps raw sensor data from event streams directly to control actions for the UAV. To learn an optimal policy under highly variable and challenging conditions, we opt for a simulation environment with domain randomization for effective transfer to real-world environments. We demonstrate the effectiveness of our approach through experiments in challenging scenarios, including fast-moving targets and changing lighting conditions, which result in improved generalization capabilities.