Research in collective robotics is motivated mainly by the possibility of achieving an efficient solution to multi-objective navigation tasks when multiple robots are employed, instead of a single robot. Several approaches have already been tried in multi-robot systems, but the bio-inspired ones are the most frequent. This paper proposes to augment an autonomous navigation system based on learning classifier systems for using in collective robotics, introducing an inter-robot communication mechanism inspired by ant stigmergy, with each robot acting independently and cooperatively. The navigation system has no innate basic behavior and all knowledge necessary to compose the decision-making artifact is evolved as a function of the environmental feedback only, during navigation. Repulsive and/or attractive pheromone trails are produced by the robots along navigation, following very simple rules. Basically, each robot has to perform obstacle avoidance and target search, and the status of the pheromonelevel at the position currently occupied by each robot will influence the coordination of the two fundamental behaviors. Experiments are performed in simulation, with comparative results indicating that the presence of the pheromone trails is responsible for significant improvements in the capture rate and in the length of the route adopted by each robot.
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