The fast flight trajectory verification algorithm for Drone Dance System

Abstract

Drone swarms are teams of autonomous unmanned aerial vehicles that act as a collective entity. We are interested in humanizing drone swarms, equip-ping them with the ability to emotionally affect human users through their nonverbal motions. We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. In this paper, we propose a fast flight trajectory verification algorithm and instant autonomous flight control alarm system, such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 20 drones. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones.