学習済み制御方策による移動目標の能動的分類
Active Classification of Moving Targets with Learned Control Policies
ドローンが深層学習分類器を用いて複数の移動目標を分類するため、強化学習で次の視点を決定し、MPCで飛行制御する手法を提案した。
著者: Álvaro Serra-Gómez, Eduardo Montijano, Wendelin Böhmer, Javier Alonso-Mora
分類: cs.RO, cs.AI
原文アブストラクト
In this paper, we consider the problem where a drone has to collect semantic information to classify multiple moving targets. In particular, we address the challenge of computing control inputs that move the drone to informative viewpoints, position and orientation, when the information is extracted using a "black-box" classifier, e.g., a deep learning neural network. These algorithms typically lack of analytical relationships between the viewpoints and their associated outputs, preventing their use in information-gathering schemes. To fill this gap, we propose a novel attention-based architecture, trained via Reinforcement Learning (RL), that outputs the next viewpoint for the drone favoring the acquisition of evidence from as many unclassified targets as possible while reasoning about their movement, orientation, and occlusions. Then, we use a low-level MPC controller to move the drone to the desired viewpoint taking into account its actual dynamics. We show that our approach not only outperforms a variety of baselines but also generalizes to scenarios unseen during training. Additionally, we show that the network scales to large numbers of targets and generalizes well to different movement dynamics of the targets.
