強化学習によるタスク指向のフォーメーション決定：攻撃群の追い込み
Task-Oriented Formation Decision via Reinforcement Learning: Herding an Attacking Swarm
複数ロボットが攻撃群を追い込むタスクにおいて、フォーメーション形状を低次元パラメータで表現し、強化学習でそのパラメータを最適化する手法を提案した。シミュレーションと実機で有効性を検証した。
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著者: Zhaozong Wang, Guibin Sun, Jinyong Chen, Rui Zhou
分類: cs.RO
原文アブストラクト
Multi-robot systems can accomplish tasks that are difficult for a single robot by organizing into task-specific formations. Different from existing studies on multi-robot shape formation, we here study the task-oriented formation decision problem, with a focus on the herding task. This task is challenging due to the attackers' superior maneuverability and their unknown strategies. To address these challenges, we propose the following novel results. First, we encode the formation shape using a low-dimensional parameter vector. This parametric representation reformulates the formation decision as a parameter optimization problem, thereby resolving the limited flexibility of predefined shapes. By optimizing these formation parameters, the defenders' maneuverability disadvantage is mitigated through a formation shape that continuously adapts to task requirements. Second, we develop a reinforcement learning-based policy to regulate the formation parameters. Trained offline in simulations covering diverse attacking strategies, the learned policy can effectively handle adversarial unpredictability during online deployment. Comparative simulations against three baselines demonstrate that our method can successfully accomplish challenging herding tasks. Additional scalability simulations further verify its applicability to simulated scenarios involving dozens of robots. We also validate the practical feasibility of our method on a physical robotic platform with 3 attackers and 7 defenders.