A mobile robot that learns its place
Neural Computation
Insect-inspired robotic homing
Adaptive Behavior
Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network Perception for Mobile Robot Guidance
Neural Network Perception for Mobile Robot Guidance
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploring artificial intelligence in the new millennium
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Autonomous mobile robot navigation systems are based on three principal kinds of techniques: map-based navigation, map-building-based navigation and mapless navigation. We propose a method for symbolic trajectory description in unknown indoor environments. The chosen form uses a panoramic description called fresco. The method uses distance measurements from a 2D laser range finder, digitises the robot's visibility area, eliminates superfluous data and reorients their presentation. The landmarks are then extracted and organised into the fresco which is validated by means of neighbourhood rules. As the robot moves in the environment, the frescoes are created and both the amount of new information a fresco carries out and its position in relation to the preceding ones are evaluated by means of two criteria. Only frescoes selected as enough informative are stored to describe the robot's route.