仮想チューブ内での時間最適な空間反復学習制御による自律ドローンレース
Autonomous Drone Racing: Time-Optimal Spatial Iterative Learning Control within a Virtual Tube
トップレーサーの訓練経験を模倣し、仮想チューブ内で軌道をオンライン反復学習することで、モデルフリーかつ低計算コストでドローンレースの時間を最短化する手法を提案した。
著者: Shuli Lv, Yan Gao, Jiaxing Che, Quan Quan
分類: cs.RO
原文アブストラクト
It is often necessary for drones to complete delivery, photography, and rescue in the shortest time to increase efficiency. Many autonomous drone races provide platforms to pursue algorithms to finish races as quickly as possible for the above purpose. Unfortunately, existing methods often fail to keep training and racing time short in drone racing competitions. This motivates us to develop a high-efficient learning method by imitating the training experience of top racing drivers. Unlike traditional iterative learning control methods for accurate tracking, the proposed approach iteratively learns a trajectory online to finish the race as quickly as possible. Simulations and experiments using different models show that the proposed approach is model-free and is able to achieve the optimal result with low computation requirements. Furthermore, this approach surpasses some state-of-the-art methods in racing time on a benchmark drone racing platform. An experiment on a real quadcopter is also performed to demonstrate its effectiveness.
