Evolution of obstacle avoidance behavior: using noise to promote robust solutions
Advances in genetic programming
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Communication in a swarm of miniature robots: the e-Puck as an educational tool for swarm robotics
SAB'06 Proceedings of the 2nd international conference on Swarm robotics
Evolution and analysis of a robot controller based on a gene regulatory network
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Robotic behavior implementation using two different differential evolution variants
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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In this paper we report our experiments with the e-puck robots for developing a communication system using evolutionary robotics. In order to do the latter we follow the evolutionary approach by using Neural Networks and Genetic Algorithms. The robots develop a communication scheme for solving tasks like: locating food areas, avoiding obstacles, approaching light sources and locating sound-sources (other robots emitting sounds). Evorobot* and Webots simulators are used as tools for computing the evolutionary process and optimization of the weights of neural controllers. As a consequence, two different kinds of neural controllers emerge. On one hand, one controller is used for robot movement; on the other hand the second controller process sound signals.