D-VAT: マイクロ航空機のためのエンドツーエンド視覚能動追跡
D-VAT: End-to-End Visual Active Tracking for Micro Aerial Vehicles
単眼カメラ画像から直接推力と角速度を計算し、深層強化学習でマイクロ航空機が対象を追跡するエンドツーエンド手法を提案。シミュレーションで高精度・衝突回避を実現し、実機クアッドロータへの転移も示した。
著者: Alberto Dionigi, Simone Felicioni, Mirko Leomanni, Gabriele Costante
分類: cs.RO
原文アブストラクト
Visual active tracking is a growing research topic in robotics due to its key role in applications such as human assistance, disaster recovery, and surveillance. In contrast to passive tracking, active tracking approaches combine vision and control capabilities to detect and actively track the target. Most of the work in this area focuses on ground robots, while the very few contributions on aerial platforms still pose important design constraints that limit their applicability. To overcome these limitations, in this paper we propose D-VAT, a novel end-to-end visual active tracking methodology based on deep reinforcement learning that is tailored to micro aerial vehicle platforms. The D-VAT agent computes the vehicle thrust and angular velocity commands needed to track the target by directly processing monocular camera measurements. We show that the proposed approach allows for precise and collision-free tracking operations, outperforming different state-of-the-art baselines on simulated environments which differ significantly from those encountered during training. Moreover, we demonstrate a smooth real-world transition to a quadrotor platform with mixed-reality.
