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2005 DARPA Grand Challenge winner Stanley performed SLAM as part of its autonomous driving system. A map generated by a SLAM Robot. Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
Robotic mapping is a discipline related to computer vision [1] and cartography.The goal for an autonomous robot is to be able to construct (or use) a map (outdoor use) or floor plan (indoor use) and to localize itself and its recharging bases or beacons in it.
Robot navigation means the robot's ability to determine its own position in its frame of reference and then to plan a path towards some goal location. In order to navigate in its environment, the robot or any other mobility device requires representation, i.e. a map of the environment and the ability to interpret that representation.
RoboMind is a simple educational programming environment with its own scripting language that allows beginners to learn the basics of computer science by programming a simulated robot. In addition to introducing common programming techniques, it also aims at offering insights in robotics and artificial intelligence .
Occupancy Grid Mapping refers to a family of computer algorithms in probabilistic robotics for mobile robots which address the problem of generating maps from noisy and uncertain sensor measurement data, with the assumption that the robot pose is known. Occupancy grids were first proposed by H. Moravec and A. Elfes in 1985.
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Robot software "drag-and-drop" interface for setting up goals, tasks and routes for autonomous robots. (Courtesy of MobileRobots Inc) PatrolBot is a robotic base used for delivery, security, sensor monitoring, inspection, and guidance tasks. It is a reference platform added onto with various carrying attachments, sensors, touchscreens and cameras.
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