ロボットナビゲーションのための深層局所軌道再計画と制御
Deep Local Trajectory Replanning and Control for Robot Navigation
大域プランナと深層学習による局所軌道計画・速度制御を組み合わせ、注意機構で歩行者を避けつつ目標へ向かう解釈可能なナビゲーションシステムを提案した。
著者: Ashwini Pokle, Roberto Martín-Martín, Patrick Goebel, Vincent Chow, Hans M. Ewald, Junwei Yang, Zhenkai Wang, Amir Sadeghian, Dorsa Sadigh, Silvio Savarese, Marynel Vázquez
分類: cs.RO, cs.AI
原文アブストラクト
We present a navigation system that combines ideas from hierarchical planning and machine learning. The system uses a traditional global planner to compute optimal paths towards a goal, and a deep local trajectory planner and velocity controller to compute motion commands. The latter components of the system adjust the behavior of the robot through attention mechanisms such that it moves towards the goal, avoids obstacles, and respects the space of nearby pedestrians. Both the structure of the proposed deep models and the use of attention mechanisms make the system's execution interpretable. Our simulation experiments suggest that the proposed architecture outperforms baselines that try to map global plan information and sensor data directly to velocity commands. In comparison to a hand-designed traditional navigation system, the proposed approach showed more consistent performance.
