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Kuckling J. Recent trends in robot learning and evolution for swarm robotics. Front Robot AI 2023; 10:1134841. [PMID: 37168882 PMCID: PMC10166233 DOI: 10.3389/frobt.2023.1134841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 03/21/2023] [Indexed: 05/13/2023] Open
Abstract
Swarm robotics is a promising approach to control large groups of robots. However, designing the individual behavior of the robots so that a desired collective behavior emerges is still a major challenge. In recent years, many advances in the automatic design of control software for robot swarms have been made, thus making automatic design a promising tool to address this challenge. In this article, I highlight and discuss recent advances and trends in offline robot evolution, embodied evolution, and offline robot learning for swarm robotics. For each approach, I describe recent design methods of interest, and commonly encountered challenges. In addition to the review, I provide a perspective on recent trends and discuss how they might influence future research to help address the remaining challenges of designing robot swarms.
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A latent space-based estimation of distribution algorithm for large-scale global optimization. Soft comput 2019. [DOI: 10.1007/s00500-018-3390-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Nitschke G, Didi S. Evolutionary Policy Transfer and Search Methods for Boosting Behavior Quality: RoboCup Keep-Away Case Study. Front Robot AI 2017. [DOI: 10.3389/frobt.2017.00062] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Duarte M, Costa V, Gomes J, Rodrigues T, Silva F, Oliveira SM, Christensen AL. Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots. PLoS One 2016; 11:e0151834. [PMID: 26999614 PMCID: PMC4801206 DOI: 10.1371/journal.pone.0151834] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 03/05/2016] [Indexed: 11/19/2022] Open
Abstract
Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.
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Affiliation(s)
- Miguel Duarte
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal
| | - Vasco Costa
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal
| | - Jorge Gomes
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- BioISI, Faculdade de Ciências, Lisbon, Portugal
| | - Tiago Rodrigues
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal
| | - Fernando Silva
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- BioISI, Faculdade de Ciências, Lisbon, Portugal
| | - Sancho Moura Oliveira
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal
| | - Anders Lyhne Christensen
- BioMachines Lab, Lisbon, Portugal
- Instituto de Telecomunicações, Lisbon, Portugal
- Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal
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