Swarms of Self-assembling Robots
Engineering Environment-Mediated Multi-Agent Systems
A Novel Approach to Swarm Bot Architecture
CAR '09 Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics
Self-Organized Aggregation Triggers Collective Decision Making in a Group of Cockroach-Like Robots
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Strengths and synergies of evolved and designed controllers: A study within collective robotics
Artificial Intelligence
A geometric approach to deploying robot swarms
Annals of Mathematics and Artificial Intelligence
GESwarm: grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Generic behaviour similarity measures for evolutionary swarm robotics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Multivariate context collection in mobile sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
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An important goal of collective robotics is the design of control systems that allow groups of robots to accomplish common tasks by coordinating without a centralized control. In this paper, we study how a group of physically assembled robots can display coherent behavior on the basis of a simple neural controller that has access only to local sensory information. This controller is synthesized through artificial evolution in a simulated environment in order to let the robots display coordinated-motion behaviors. The evolved controller proves to be robust enough to allow a smooth transfer from simulated to real robots. Additionally, it generalizes to new experimental conditions, such as different sizes/shapes of the group and/or different connection mechanisms. In all these conditions the performance of the neural controller in real robots is comparable to the one obtained in simulation