Fuzzy counter-ant for avoiding the stagnation of multirobot exploration

Abstract

Since swarm intelli g ence allows self-or g anization into an unfamiliar environment and adapting behaviors throu g h sim p le individuals' interactions, we p ro p ose to realize a swarm multirobot organization with a fuzzy control. We introduce in this p aper a fuzzy system for avoidin g the collaboration sta g nation and to im p rove the counter-ant al g orithm (CAA). The robots' collaborative behavior is based on a hybrid approach combinin g the CAA and a fuzzy system learned by MAGAD-BFS (Multi-a g ent Genetic Al g orithm for the Design of Beta Fuzzy System). A series of simulations enables us to discuss and validate both the effectiveness of the hybrid a pp roach to the problem of environment exploration (i.e., for the p ur pose of cleanin g an area) as well as the usefulness of MAGAD-BFS for learning the fuzzy knowled g e base while tunin g it and reducin g its number of rules.

Optimization through coupling learning capability and imitation strategy in a Multi-Robot System

“…The set of all the connected TCs and PCs forms a graph which constitute in itself a non-cartesian cognitive map (see Fig.4). A passive forgetting factor (similar to what can be achieved in swarm-intelligence systems, except that shared knowledge does not use the physical space [19], [20] as repository but uses individual cognitive maps instead) is set on the transition cells through the synaptic weights between couples of connected transition cells (which can vary from 0 -fully forgotten to 1 -just visited). Transition cells which were recently visited are better remembered and motivation to take the corresponding edge is higher than transition cells which have not been for a long time.…”

Emergent complex behaviors for swarm robotic systems by local rules

“…The modified version includes a solution for stagnation recovery using pheromone positions. In recent years, research has been oriented more towards the development of real-time systems and particularly the study of hybrid methods to ensure scalability of behavior with respect to the dynamic nature of the environment, in [20] the authors present a fuzzy system for avoiding the collaboration stagnation and to improve the counter-ant algorithm . The robots collaborative behavior is based on a hybrid approach combining the CAA and a fuzzy system learned by MAGAD-BFS (Multi-agent Genetic Algorithm for the Design of Beta Fuzzy System).…”