Design and simulation of the emergent behavior of small drones swarming for distributed target localization

•Stigmergy provides an effective coordination of drones flocking for target search.•No information on the search field layout or centralized control is required.•Differential evolution makes the swarm adaptive to different mission complexities.•Results on synthetic and real-world scenarios show scal...

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Bibliographic Details
Published inJournal of computational science Vol. 29; pp. 19 - 33
Main Authors Alfeo, Antonio L., Cimino, Mario G.C.A., De Francesco, Nicoletta, Lega, Massimiliano, Vaglini, Gigliola
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.11.2018
Subjects
Differential evolution
Drone
Flocking
Stigmergy
Swarm intelligence
Target search
Differential evolution
Swarm intelligence
Flocking
Drone
Stigmergy
Target search
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Summary:•Stigmergy provides an effective coordination of drones flocking for target search.•No information on the search field layout or centralized control is required.•Differential evolution makes the swarm adaptive to different mission complexities.•Results on synthetic and real-world scenarios show scalability and efficiency. A swarm of autonomous drones with self-coordination and environment adaptation can offer a robust, scalable and flexible manner to localize objects in an unexplored, dangerous or unstructured environment. We design a novel coordination algorithm combining three biologically inspired processes: stigmergy, flocking and evolution. Stigmergy, a form of coordination exhibited by social insects, is exploited to attract drones in areas with potential targets. Flocking enables efficient cooperation between flock mates upon target detection, while keeping an effective scan. The two mechanisms can interoperate if their structural parameters are correctly tuned for a given scenario. Differential evolution adapts the swarm coordination according to environmental conditions. The performance of the proposed algorithm is examined with synthetic and real-world scenarios.
ISSN:1877-7503
1877-7511
DOI:10.1016/j.jocs.2018.09.014