Obtaining emergent behaviors for swarm robotics singling with deep reinforcement learning
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http://hdl.handle.net/10045/135187
Título: | Obtaining emergent behaviors for swarm robotics singling with deep reinforcement learning |
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Autor/es: | Arques Corrales, Pilar | Aznar Gregori, Fidel | Pujol, Mar | Rizo, Ramón |
Grupo/s de investigación o GITE: | Informática Industrial e Inteligencia Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
Palabras clave: | Swarm robotics | Deep reinforcement learning | Shepherding | Singling |
Fecha de publicación: | 30-mar-2023 |
Editor: | Taylor & Francis |
Cita bibliográfica: | Advanced Robotics. 2023, 37(11): 702-717. https://doi.org/10.1080/01691864.2023.2194952 |
Resumen: | Isolating (singling) an individual from a group can be essential for protection, rescue or capture tasks. In this paper a system with multiple shepherds who must coordinate the sheep to achieve a specific singleness is proposed. We present a realistically modeled system that will be finally tested in a real robotic system. We want to encourage the adaptability of the system and provide different solutions by promoting the emergence of the swarm. In this line we will focus on the use of reinforcement learning, avoiding a manual design of the behavior in order to not restrict the resulting behaviors and to facilitate their adaptation. A detailed MDP model will be specified as well as the keys to reduce its dimensionality and facilitate its training. We will check the results of the obtained singling policy with respect to a greedy policy and focus on evaluating different behavioral strategies that can solve the problem in different ways. In addition, one of the obtained policies will be analyzed in detail to check both its robustness and its scalability with respect to the number of shepherds and sheep. This policy will be finally tested on a physical robotic swarm. |
Patrocinador/es: | This work was supported by the Ministerio de Ciencia, Innovación y Universidades (Spain) [project RTI2018-096219-BI00]. Project co-financed with FEDER funds. |
URI: | http://hdl.handle.net/10045/135187 |
ISSN: | 0169-1864 (Print) | 1568-5535 (Online) |
DOI: | 10.1080/01691864.2023.2194952 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2023 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1080/01691864.2023.2194952 |
Aparece en las colecciones: | INV - i3a - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Arques_etal_2023_AdvRobot_final.pdf | Versión final (acceso restringido) | 3,3 MB | Adobe PDF | Abrir Solicitar una copia |
Arques_etal_2023_AdvRobot_preprint.pdf | Preprint (acceso abierto) | 3,18 MB | Adobe PDF | Abrir Vista previa |
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