Wind Field Modeling for Formation Planning in Multi-Drone Systems

In multi-drone systems such as drone light shows, drones move in formation while avoiding collisions. However, few existing formation planning algorithms consider the wind fields of drones during planning. Since the wind field effect is prominent when drones have to fly close to each other, we cannot ignore the effect during planning. In this paper, we extend the reservation system in autonomous intersection management for grid-based formation planning by including a new type of reservation called non-exclusive reservations specifically for handling wind fields. We train a deep learning model to predict the deviation of a drone’s trajectory when the drone enters the wind field of another drone and then use the reservation grid to prevent collision. Based on the reservation system, we develop a new formation planning algorithm that focuses on adjusting the start times of motion plans to avoid collision. Our experimental results show that trajectory prediction can help make better decisions in task assignments for minimizing makespans.