  Animal swarms displaying a variety of typical flocking patterns would not
exist without underlying safe, optimal and stable dynamics of the individuals.
The emergence of these universal patterns can be efficiently reconstructed with
agent-based models. If we want to reproduce these patterns with artificial
systems, such as autonomous aerial robots, agent-based models can also be used
in the control algorithm of the robots. However, finding the proper algorithms
and thus understanding the essential characteristics of the emergent collective
behaviour of robots requires the thorough and realistic modeling of the robot
and the environment as well. In this paper, first, we present an abstract
mathematical model of an autonomous flying robot. The model takes into account
several realistic features, such as time delay and locality of the
communication, inaccuracy of the on-board sensors and inertial effects. We
present two decentralized control algorithms. One is based on a simple
self-propelled flocking model of animal collective motion, the other is a
collective target tracking algorithm. Both algorithms contain a viscous
friction-like term, which aligns the velocities of neighbouring agents parallel
to each other. We show that this term can be essential for reducing the
inherent instabilities of such a noisy and delayed realistic system. We discuss
simulation results about the stability of the control algorithms, and perform
real experiments to show the applicability of the algorithms on a group of
autonomous quadcopters.
