The map-building and exploration strategies of a simple sonar-equipped mobile robot
The map-building and exploration strategies of a simple sonar-equipped mobile robot
Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
Map learning and high-speed navigation in RHINO
Artificial intelligence and mobile robots
Robot Motion Planning
Introduction to Robotics
MICAI '00 Proceedings of the Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
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A mobile robot must explore its workspace in order to learn a map of its environment. Given the perceptual limitations and accuracy of its sensors, the robot has to stay close to obstacles in order to track its position and never get lost. This paper describes a new method for exploring and navigating autonomously in indoor environments. It merges a local strategy, similar to a wall following strategy to keep the robot close to obstacles, within a global search frame, based on a dynamic programming algorithm. This hybrid approach takes advantages of local strategies that consider perceptual limitations of sensors without losing the completeness of a global search. These methods for exploring and navigating are tested using a mobile robot simulator with very good results.