Model predictive control of autonomous drone considering model of birds aimed at inducing a flock of birds

In recent years, bird strikes are increasing with the increase in the number of airliner flights. Therefore, measures against bird strikes are required. Collisions between an aircraft and a flock of birds during takeoff and landing are numerous and can cause serious damage. Related works have been conducted to prevent entry of a flock of birds into the runway. The purpose of this study was to guide a flock of birds into avoiding the runway using an autonomous drone. Guidance was successful in simulations and experiments, provided that the drone was located near a flock of birds. Therefore, in this study, assuming that the drones are placed around the runway and far from the flock, we propose a control method to move the drone towards the flock and guide the birds to prevent bird strikes. In order to realize interception and guidance under such conditions, we predict the trajectories of the bird flock using a model of birds. Setting the target position of the drone on the predicted trajectories allows the drone to intercept the birds without unnecessary movement of the drone. Because the drone is in the same speed range as the birds, we determine the drone’s control input so as to satisfy the velocity constraint using model predictive control (MPC). In this study, we propose a control method for autonomous drones that considers the model of birds, aimed at guiding a flock of birds to prevent bird strike. We verify the performance of the proposed method by conducting numerical analysis based on a model of a realistic runway.