An adaptive state space architecture is combined with diktiometric representation to provide the framework for designing a robot mapping system with flexible navigation planning tasks. This involves indexing waypoints described as expectations, geometric indexing, and perceptual indexing. Matching and updating the robot's projected position and sensory inputs with indexing waypoints involves matchers, dynamic priorities, transients, and waypoint restructuring. The robot's map learning can be opganized around the principles of passive mapping.
Passive mapping and intermittent exploration for mobile robots
1994-03-01
Conference paper
No indication
English
Gaussian processes autonomous mapping and exploration for range-sensing mobile robots
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