authorCsaba Virágh, Gábor Vásárhelyi, Norbert Tarcai, Tamás Szörényi, Gergő Somorjai, Tamás Nepusz and Tamás Vicsek
year2014
titleFlocking algorithm for autonomous flying robots
journalBioinspiration & Biomimetics
volume9
number2
pages025012
projectcollective_motion_of_flying_robots, flockctrl
doi10.1088/1748-3182/9/2/025012
project_groupcollective_robotics, hardware_development
url[url]
pdf[pdf]
abstract
Animal swarms displaying a variety of typical flocking patterns would not exist without the 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 their control algorithms. However, finding the proper algorithms and thus understanding the essential characteristics of the emergent collective behaviour requires thorough and realistic modeling of the robot and also the environment. In this paper, we first 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 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 on the stability of the control algorithms, and perform real experiments to show the applicability of the algorithms on a group of autonomous quadcopters. In our case, bio-inspiration works in two ways. On the one hand, the whole idea of trying to build and control a swarm of robots comes from the observation that birds tend to flock to optimize their behaviour as a group. On the other hand, by using a realistic simulation framework and studying the group behaviour of autonomous robots we can learn about the major factors influencing the flight of bird flocks.

BibTex record:

@article{viragh2014flocking,
author = {Csaba Virágh and Gábor Vásárhelyi and Norbert Tarcai and Tamás Szörényi and Gergő Somorjai and Tamás
Nepusz and Tamás Vicsek},
year = {2014},
title = {Flocking algorithm for autonomous flying robots},
journal = {Bioinspiration & Biomimetics},
volume = {9},
number = {2},
pages = {025012},
project = {collective_motion_of_flying_robots, flockctrl},
doi = {10.1088/1748-3182/9/2/025012},
project_group = {collective_robotics, hardware_development},
url = {http://stacks.iop.org/1748-3190/9/i=2/a=025012},
pdf = {https://hal.elte.hu/flocking/browser/trunk/public/references/vasarhelyi/viragh2014flocking.pdf?forma
t=raw},
abstract = {Animal swarms displaying a variety of typical flocking patterns would not exist without the
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 their control algorithms. However, finding the proper algorithms and thus understanding the
essential characteristics of the emergent collective behaviour requires thorough and realistic
modeling of the robot and also the environment. In this paper, we first 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 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
on the stability of the control algorithms, and perform real experiments to show the applicability
of the algorithms on a group of autonomous quadcopters. In our case, bio-inspiration works in two
ways. On the one hand, the whole idea of trying to build and control a swarm of robots comes from
the observation that birds tend to flock to optimize their behaviour as a group. On the other hand,
by using a realistic simulation framework and studying the group behaviour of autonomous robots we
can learn about the major factors influencing the flight of bird flocks.},
}