  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.
