hen observing an Autonomous Unmanned Aerial Vehicle(UAV) race, one would be hard-pressed to call it racing as the actual velocities attained are extremely low. This article addresses this shortcoming by proposing a method of generating and executing a racing trajectory for a UAV, through a series of position objectives representative of a racing environment, with the goal of significantly improving the velocity when compared to the current norm of PID controllers. The method consists of applying Nonlinear Model Predictive Control with the capability of dynamically updating the position goal based upon internal state estimation to generate a set of inputs for a UAV. To prove the viability of the proposed method we test by using numerical simulations, a flight simulator environment(Gazebo) and a series of real-world flight tests on the Bebop1 UAV. Through 2 iterations of the testing process it is proven that the method is able to significantly(approximately 1s) decrease the flight time through both simple and more complex short range manoeuvres(2m-4m). However model errors and an inability to fully control thrust on the UAV introduce a significant and consistent position error.

When observing an Autonomous Unmanned Aerial Vehicle(UAV) race, one would be hard-pressed to call it racing as the actual velocities attained are extremely low. This article addresses this shortcoming by proposing a method of generating and executing a racing trajectory for a UAV, through a series of position objectives representative of a racing environment, with the goal of significantly improving the velocity when compared to the current norm of PID controllers. The method consists of applying Nonlinear Model Predictive Control with the capability of dynamically updating the position goal based upon internal state estimation to generate a set of inputs for a UAV. To prove the viability of the proposed method we test by using numerical simulations, a flight simulator environment(Gazebo) and a series of real-world flight tests on the Bebop1 UAV. Through 2 iterations of the testing process it is proven that the method is able to significantly decrease the flight time ()through both simple and more complex short range manoeuvres(2m-4m). However model errors and an inability to fully control thrust on the UAV introduce a significant and consistent position error.