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Collision avoidance effects on the mobility of a UAV swarm using chaotic ant colony with model predictive control.

Dentler, Jan; Rosalie, Martin; Danoy, Grégoire; Bouvry, Pascal; Kannan, Somasundar; Olivares-Mendez, Miguel A.; Voos, Holger

Authors

Jan Dentler

Martin Rosalie

Grégoire Danoy

Pascal Bouvry

Miguel A. Olivares-Mendez

Holger Voos



Abstract

The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance the area coverage of such UAV swarms, a novel mobility model has been presented in previous work, combining an Ant Colony algorithm with chaotic dynamics (CACOC). This work is extending CACOC by a Collision Avoidance (CA) mechanism and testing its efficiency in terms of area coverage by the UAV swarm. For this purpose, CACOC is used to compute UAV target waypoints which are tracked by model predictively controlled UAVs. The UAVs are represented by realistic motion models within the virtual robot experimentation platform (V-Rep). This environment is used to evaluate the performance of the proposed CACOC with CA algorithm in an area exploration scenario with 3 UAVs. Finally, its performance is analyzed using metrics.

Citation

DENTLER, J., ROSALIE, M., DANOY, G., BOUVRY, P., KANNAN, S., OLIVARES-MENDEZ, M.A. and VOOS, H. 2019. Collision avoidance effects on the mobility of a UAV swarm using chaotic ant colony with model predictive control. Journal of intelligent and robotic systems [online], 93(1-2), pages 227-243. Available from: https://doi.org/10.1007/s10846-018-0822-8

Journal Article Type Article
Acceptance Date Feb 11, 2018
Online Publication Date Apr 20, 2018
Publication Date Feb 15, 2019
Deposit Date Feb 23, 2021
Publicly Available Date Feb 23, 2021
Journal Journal of intelligent and robotic systems
Print ISSN 0921-0296
Electronic ISSN 1573-0409
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 93
Issue 1-2
Pages 227-243
DOI https://doi.org/10.1007/s10846-018-0822-8
Keywords Control and Systems Engineering; Mechanical Engineering; Electrical and Electronic Engineering; Industrial and Manufacturing Engineering; Software; Artificial Intelligence
Public URL https://rgu-repository.worktribe.com/output/1206011

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Copyright Statement
This pre-copyedited version is made available under the Springer terms of reuse for AAMs: https://www.springer.com/gp/open-access/publication-policies/aam-terms-of-use





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