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Authors: Pedro Romano 1 ; 2 ; Luís Nunes 1 ; 3 and Sancho Oliveira 1 ; 3 ; 2

Affiliations: 1 Iscte - Instituto Universitário de Lisboa, Av. Forças Armadas, Lisboa, Portugal ; 2 Instituto de Telecomunicações, IT Iscte, Lisboa, Portugal ; 3 ISTAR Iscte, Lisboa, Portugal

Keyword(s): Evolutionary Robotics, Multirobot Systems, Cooperation, Perception, Object Identification, Artificial Intelligence.

Abstract: Training of robotic swarms is usually done for a specific task and environment. The more specific the training is, the more the likelihood of reaching a good performance. Still, flexibility and robustness are essential for autonomy, enabling the robots to adapt to different environments. In this work, we study and compare approaches to robust training of a small simulated swarm on a task of cooperative identification of moving objects. Controllers are obtained via evolutionary methods. The main contribution is the test of the effectiveness of training in multiple environments: simplified versions of terrain, marine and aerial environments, as well as on ideal, noisy and hybrid (mixed environment) scenarios. Results show that controllers can be generated for each of these scenarios, but, contrary to expectations, hybrid evolution and noisy training do not, in general, generate better controllers for the different scenarios. Nevertheless, the hybrid controller reaches a performance lev el par with specialized controllers in several scenarios, and can be considered a more robust solution. (More)

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Paper citation in several formats:
Romano, P.; Nunes, L. and Oliveira, S. (2023). Hybrid Training to Generate Robust Behaviour for Swarm Robotics Tasks. In Proceedings of the 15th International Joint Conference on Computational Intelligence - ECTA; ISBN 978-989-758-674-3; ISSN 2184-3236, SciTePress, pages 265-277. DOI: 10.5220/0012193300003595

@conference{ecta23,
author={Pedro Romano. and Luís Nunes. and Sancho Oliveira.},
title={Hybrid Training to Generate Robust Behaviour for Swarm Robotics Tasks},
booktitle={Proceedings of the 15th International Joint Conference on Computational Intelligence - ECTA},
year={2023},
pages={265-277},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012193300003595},
isbn={978-989-758-674-3},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - ECTA
TI - Hybrid Training to Generate Robust Behaviour for Swarm Robotics Tasks
SN - 978-989-758-674-3
IS - 2184-3236
AU - Romano, P.
AU - Nunes, L.
AU - Oliveira, S.
PY - 2023
SP - 265
EP - 277
DO - 10.5220/0012193300003595
PB - SciTePress