動的に実行可能な深層強化学習方策による密な移動群衆中のロボットナビゲーション
Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation in Dense Mobile Crowds
DWAの動的制約充足性と深層強化学習の移動障害物対応を組み合わせ、障害物運動を低次元観測空間に埋め込むことで衝突を大幅に減らすナビゲーション方策を提案した。
著者: Utsav Patel, Nithish Kumar, Adarsh Jagan Sathyamoorthy, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present a novel Deep Reinforcement Learning (DRL) based policy to compute dynamically feasible and spatially aware velocities for a robot navigating among mobile obstacles. Our approach combines the benefits of the Dynamic Window Approach (DWA) in terms of satisfying the robot's dynamics constraints with state-of-the-art DRL-based navigation methods that can handle moving obstacles and pedestrians well. Our formulation achieves these goals by embedding the environmental obstacles' motions in a novel low-dimensional observation space. It also uses a novel reward function to positively reinforce velocities that move the robot away from the obstacle's heading direction leading to significantly lower number of collisions. We evaluate our method in realistic 3-D simulated environments and on a real differential drive robot in challenging dense indoor scenarios with several walking pedestrians. We compare our method with state-of-the-art collision avoidance methods and observe significant improvements in terms of success rate (up to 33\% increase), number of dynamics constraint violations (up to 61\% decrease), and smoothness. We also conduct ablation studies to highlight the advantages of our observation space formulation, and reward structure.
