Coordinated flight of multiple drones allows to achieve tasks faster such as search and rescue and infrastructure inspection. Thus, pushing the state-of-the-art of aerial swarms in navigation speed and robustness is of tremendous benefit. In particular, being able to account for unexplored/unknown environments when planning trajectories allows for safer flight. In this work, we propose the first high-speed, decentralized, and synchronous motion planning framework (HDSM) for an aerial swarm that explicitly takes into account the unknown/undiscovered parts of the environment. The proposed approach generates an optimized trajectory for each planning agent that avoids obstacles and other planning agents while moving and exploring the environment. The only global information that each agent has is the target location. The generated trajectory is high-speed, safe from unexplored spaces, and brings the agent closer to its goal. The proposed method outperforms four recent state-of-the-art methods in success rate (100% success in reaching the target location), flight speed (67% faster), and flight time (42% lower). Finally, the method is validated on a set of Crazyflie nano-drones as a proof of concept.

翻译：多架无人机协同飞行能够更快地完成搜救和基础设施巡检等任务。因此，提升空中蜂群在导航速度和鲁棒性方面的技术水平具有巨大价值。特别是在规划轨迹时考虑未探索/未知环境，能实现更安全的飞行。本文首次提出一种针对空中蜂群的高速、分散式且同步的运动规划框架（HDSM），该框架显式考虑了环境中未知/未发现的部分。所提方法为每个规划智能体生成一条优化轨迹，使其在移动和探索环境过程中避开障碍物及其他规划智能体。每个智能体拥有的唯一全局信息是目标位置。生成的轨迹高速、远离未探索空间，并引导智能体接近目标。在成功率（100%到达目标位置）、飞行速度（快67%）和飞行时间（低42%）方面，该方法优于四种近期最先进方法。最后，作为概念验证，该方法在一组Crazyflie纳米无人机上得到验证。