Distributed data-driven UAV formation control via evolutionary games: experimental results

This work proposes a novel data-driven distributed formation-control approach based on multi-population evolutionary games, which is structured in a leader-follower scheme. The methodology considers a time-varying communication graph that describes how the multiple agents share information to each o...

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Detalles Bibliográficos
Autores: Barreiro Gómez, Julián, Mas, Ignacio, Giribet, Juan Ignacio, Moreno, Patricio, Ocampo-Martínez, Carlos|||0000-0001-9251-6044, Sánchez Peña, Ricardo Salvador, Quijano Silva, Nicanor
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/356231
Acceso en línea:https://hdl.handle.net/2117/356231
https://dx.doi.org/10.1016/j.jfranklin.2021.05.002
Access Level:acceso abierto
Palabra clave:Game theory
Drone aircraft
Jocs, Teoria de
Avions no tripulats
Data-driven control
Evolutionary dynamics
Mobile robots
Formation control
UAVs
Real implementation
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
Descripción
Sumario:This work proposes a novel data-driven distributed formation-control approach based on multi-population evolutionary games, which is structured in a leader-follower scheme. The methodology considers a time-varying communication graph that describes how the multiple agents share information to each other. We present stability guarantees for configurations given by time-varying interaction networks, making the proposed method suitable for real-world problems where communication constraints change along the time. Additionally, the proposed formation controller allows for an agent to leave or enter the group without the need to modify the behaviors of other agents in the group. This game-theoretical approach is evaluated through numerical simulations and real outdoors experimental results using a fleet of aerial autonomous vehicles, showing the control performance.