Mobile robot navigation using neural networks and nonmetrical environmental models

A reasoning and control architecture for vision-guided navigation that makes a robot more humanlike is presented. This system, called NEURO-NAV, discards the more traditional geometrical representation of the environment, and instead uses a semantically richer nonmetrical representation in which a hallway is modeled by the order of appearance of various landmarks and by adjacency relationships. With such a representation, it becomes possible for the robot to respond to commands such as, 'follow the corridor and turn right at the second T junction'. This capability is achieved by an ensemble of neural networks whose activation and deactivation are controlled by a rule-based supervisory controller. The individual neural networks in the ensemble are trained to interpret visual information and perform primitive navigational tasks such as hallway following and landmark detection.<
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