A Multi-robot Surveillance System Simulated in Gazebo
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Learning to Coordinate Multi-robot Competitive Systems by Stimuli Adaptation
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Genetic team composition and level of selection in the evolution of cooperation
IEEE Transactions on Evolutionary Computation
Complex behaviours through modulation in autonomous robot control
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Coordinated multi-robot exploration
IEEE Transactions on Robotics
Discrete-time backpropagation for training synaptic delay-based artificial neural networks
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper describes an approach for the progressive construction of controllers for sets of robots performing collective behaviors. The procedure is based on the incremental construction through evolution of a neural multilevel behavior architecture where the higher-level behaviors modulate the actuation of the lower-level ones. This hybridization permits simplifying the design of the behavior controllers and allows obtaining them in evolutionary processes without making the search space huge. From a cognitive point of view, the procedure could be thought of as an incremental learning procedure where the robot first learns basic responses and then uses them within more elaborate decision and actuation processes progressively increasing complexity.