動的障害物環境下でのMAV向けリアルタイム長距離軌道再計画
Real-Time Long Range Trajectory Replanning for MAVs in the Presence of Dynamic Obstacles
ドローンが動的障害物を検知すると、RRT*を改良してミリ秒単位で現在位置から長距離の軌道を再計画し、速度・ヨー角を制御器へ送って自律飛行するシステムを構築・実機検証した。
著者: Geesara Kulathunga, Roman Fedorenko, Sergey Kopylov, Alexandr Klimchik
分類: cs.RO
原文アブストラクト
Real-time long-range local planning is a challenging task, especially in the presence of dynamics obstacles. We propose a complete system which is capable of performing the local replanning in real-time. Desired trajectory is needed in the system initialization phase; system starts initializing sub-components of the system including point cloud processor, trajectory estimator and planner. Afterwards, the multi-rotary aerial vehicle starts moving on the given trajectory. When it detects obstacles, it replans the trajectory from the current pose to pre-defined distance incorporating the desired trajectory. Point cloud processor is employed to identify the closest obstacles around the vehicle. For replanning, Rapidly-exploring Random Trees (RRT*) is used with two modifications which allow planning the trajectory in milliseconds scales. Once we replanned the desired path, velocity components(x,y and z) and yaw rate are calculated. Those values are sent to the controller at a constant frequency to maneuver the vehicle autonomously. Finally, we have evaluated each of the components separately and tested the complete system in the simulated and real environments.
