Authors:
Mario G. C. A. Cimino
1
;
Massimiliano Lega
2
;
Manilo Monaco
1
and
Gigliola Vaglini
1
Affiliations:
1
Department of Information Engineering, University of Pisa, 56122 Pisa and Italy
;
2
Department of Engineering University of Naples “Parthenope”, 80143 Naples and Italy
Keyword(s):
UAV, Swarm Intelligence, Stigmergy, Flocking, Differential Evolution, Target Detection, Target Tracking.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Pattern Recognition
;
Robotics
;
Software Engineering
Abstract:
This paper focuses on the problem of coordinating multiple UAVs for distributed targets detection and tracking, in different technological and environmental settings. The proposed approach is founded on the concept of swarm behavior in multi-agent systems, i.e., a self-formed and self-coordinated team of UAVs which adapts itself to mission-specific environmental layouts. The swarm formation and coordination are inspired by biological mechanisms of flocking and stigmergy, respectively. These mechanisms, suitably combined, make it possible to strike the right balance between global search (exploration) and local search (exploitation) in the environment. The swarm adaptation is based on an evolutionary algorithm with the objective of maximizing the number of tracked targets during a mission or minimizing the time for target discovery. A simulation testbed has been developed and publicly released, on the basis of commercially available UAVs technology and real-world scenarios. Experiment
al results show that the proposed approach extends and sensibly outperforms a similar approach in the literature.
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