  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 (97% faster), and flight time (50% lower). Finally, the
method is validated on a set of Crazyflie nano-drones as a proof of concept.
