A cooperative architecture for swarm robotic based on dynamic fuzzy cognitive maps

This work presents a cooperative architecture for navigation of a swarm of robots based on Dynamic Fuzzy Cognitive Maps (DFCM). This architecture is used to develop homogeneous autonomous robots without any type of global controllers. The developed autonomous navigation system has skills for learning, self-adaptation, behavior management and cooperative data share. We adopt a multi-agent approach, inspired by Brooks subsumption architecture, which allows hierarchical management of actions and parallel processing. Reinforcement learning is used to self-tune the navigation system parameters allowing the DFCM model to be self-adaptive. Two strategies are analyzed for data and experience exchange between agents (robots). The first one is based on a navigation memory sharing and the other is a bio inspired by ant's behavior strategy. Results are presented with both strategies and comparisons of their performance are carried out in a virtual environment.