Agile Trajectory Planning and Large Obstacle Avoidance for Formation Flight Using a Virtual Core

Abstract

Current methods for formation flight primarily focus on maintaining formations, often neglecting the swarm's agility. Furthermore, most of these approaches fail to leverage global information from the swarm for obstacle avoidance, making them incapable of generating efficient and safe trajectories in large obstacle scenarios. To address these limitations, this letter proposes a novel swarm trajectory planning framework that utilizes a virtual core to control the swarm. We employ virtual core penalties and dynamic maximum speed allocation to strike a balance between swarm flexibility and formation keeping, allowing the drones to avoid obstacles more smoothly and safely while maintaining formation stability. For large obstacle avoidance, we design a collaborative large obstacle boundary search strategy and a global swarm planning method to enable the rapid and safe generation of drone trajectories. To validate the performance of the proposed method, we develop a comprehensive set of experimental scenarios that include both simulations and real-world environments. The experimental results confirm the effectiveness of our approach.