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Abstract.Here we propose an architecture for an autonomous mobile agent that explores while mapping a two-dimensional environment. The map is a discretized model for the localization of obstacles, on top of which a harmonic potential field is computed. The potential field serves as a fundamental link between the modeled (discrete) space and the real (continuous) space where the agent operates. It indicates safe paths towards non-explored regions. Harmonic functions were originally used as global path planners in mobile robotics. In this paper, we extend its functionality to environment exploration. We demonstrate our idea through experimental results obtained using a Nomad 200 robot platform.

We present a way to store and to recall different environment navigation maps in a neural network. The model is built upon the idea that a navigation map can be written as the solution of the Laplace's problem with suitable boundary conditions applied to obstacles and goals in the environment. The inherent compression of information coming from that allows us to have good storage performances with a reduced number of synaptic connections.