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It is often necessary for drones to complete delivery, photography, andrescue in the shortest time to increase efficiency. Many autonomous drone racesprovide platforms to pursue algorithms to finish races as quickly as possiblefor the above purpose. Unfortunately, existing methods often fail to keeptraining and racing time short in drone racing competitions. This motivates usto develop a high-efficient learning method by imitating the trainingexperience of top racing drivers. Unlike traditional iterative learning controlmethods for accurate tracking, the proposed approach iteratively learns atrajectory online to finish the race as quickly as possible. Simulations andexperiments using different models show that the proposed approach ismodel-free and is able to achieve the optimal result with low computationrequirements. Furthermore, this approach surpasses some state-of-the-artmethods in racing time on a benchmark drone racing platform. An experiment on areal quadcopter is also performed to demonstrate its effectiveness.

Autonomous Drone Racing: Time-Optimal Spatial Iterative Learning Control within a Virtual Tube