Reference : An Evolutionary Algorithm to Optimise a Distributed UAV Swarm Formation System
Scientific journals : Article
Engineering, computing & technology : Computer science
An Evolutionary Algorithm to Optimise a Distributed UAV Swarm Formation System
Stolfi Rosso, Daniel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PCOG >]
Danoy, Grégoire mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Applied Sciences
Metaheuristics for Real-World Optimization Problems
[en] evolutionary algorithm ; crossover operator ; UAV ; swarm robotics ; argos simulator ; formation control
[en] In this article, we present a distributed robot 3D formation system optimally parameterised by a hybrid evolutionary algorithm (EA) in order to improve its efficiency and robustness. To achieve that, we first describe the novel distributed formation algorithm3 (DFA3), the proposed EA, and the two crossover operators to be tested. The EA hyperparameterisation is performed by using the irace package and the evaluation of the three case studies featuring three, five, and ten unmanned aerial vehicles (UAVs) is performed through realistic simulations by using ARGoS and ten scenarios evaluated in parallel to improve the robustness of the configurations calculated. The optimisation results, reported with statistical significance, and the validation performed on 270 unseen scenarios show that the use of a metaheuristic is imperative for such a complex problem despite some overfitting observed under certain circumstances. All in all, the UAV swarm self-organised itself to achieve stable formations in 95% of the scenarios studied with a plus/minus ten percent tolerance.
Fonds National de la Recherche - FnR
FnR ; FNR14762457 > Gregoire Danoy > ADARS > Automating The Design Of Autonomous Robot Swarms > 01/05/2021 > 30/04/2024 > 2020

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