Autonomous learning architecture for environmental mapping
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.
exploration harmonic functions navigation mapping of unknown environment