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.

翻译：无人机常需在最短时间内完成配送、摄影和救援任务，以提升效率。众多自主无人机竞速赛事为此提供平台，用以探索能最快完成赛程的算法。然而，现有方法在无人机竞速竞赛中常难以同时缩短训练时间与竞速时间。受顶级赛车手训练经验的启发，本文提出一种高效学习方法。不同于传统迭代学习控制追求精确跟踪，该方法在线迭代学习轨迹以最快完成赛程。采用多种模型的仿真与实验表明，该方法无需模型先验，且能以低计算需求实现最优结果。此外，该方法在基准无人机竞速平台上的竞速时间超越了部分现有最优方法。通过真实四旋翼飞行器实验进一步验证了其有效性。