Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

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...

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Bibliographic Details
Published in:PloS one 2016-03, Vol.11 (3), p.e0151834
Main Authors: Duarte, Miguel, Costa, Vasco, Gomes, Jorge, Rodrigues, Tiago, Silva, Fernando, Oliveira, Sancho Moura, Christensen, Anders Lyhne
Format: Article
Language:English
Subjects:
Analysis
Artificial intelligence
Behavior
Biology and Life Sciences
Cluster Analysis
Clustering
Computer and Information Sciences
Computer simulation
Controllers
Design
Engineering and Technology
Environmental monitoring
Evolution
Experiments
Homing
Homing behavior
International conferences
Laboratories
Neural networks
Physical Sciences
Portugal
Research and Analysis Methods
Researchers
Robotics
Robots
Simulation
Studies
Swarm intelligence
Task Performance and Analysis
Telecommunications
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0151834