Distributed Three Dimensional Flocking of Autonomous Drones

Potential field approaches have been often used to describe and model interactions within a swarm of robots performing collective motion, also called flocking. Despite the high number of proposed approaches, most have only been tested in simulation and among the minority tested on real robots, even fewer abandoned the laboratory boundaries in favor of real-world scenarios. In this work, we propose a decentralized flocking approach that builds over the classical potential field models and that is proved to work well both in simulated and real-world environments. Each robot in the swarm relies on limited information and can only perceive its local neighbors through limited communication of noisy position information. No information on individual drone orientations, velocities, or accelerations is exchanged or needed. The novel experimental achievement of this paper is the realization of collective motion in three dimensions with the above sensing limitations. The swarm dynamically adapts to the environment by keeping a preferred distance from the ground and by changing formation. To show the general applicability of the proposed control algorithm, we study how it performs with the use of different potential functions proposed in the literature and by comparing them via extensive evaluation of the results in a realistic simulated environment. Lastly, we compare the performances of the proposed approach and of the different potentials on a real-drone swarm of up to fourteen robots flying both in two and three dimensional formations and in a challenging outdoor environment.