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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 economics is the study of the market for robots.Robot markets function through the interaction of robot makers and robot users. As (in part) a factor of production, robots are complements and/or substitutes for other factors, such as labor and (non-robot) capital goods.
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.
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.
Just days later, it held its highly anticipated "We, Robot" event, during which it showed off the progress it has made on self-driving vehicles and humanoid robots. And on Oct. 23, it will issue ...
The field of robotic explorations draws from various fields of information gathering and decision theory, and have been studied as far back as the 1950s. The earliest work in robotic exploration was done in the context of simple finite state automata known as bandits, where algorithms were designed to distinguish and map different states in a ...
The Biden Administration’s move on Jan. 13 to curb exports on the advanced computer chips used to power artificial intelligence (AI) arrived in the wake of two major events over the Christmas ...
MAP estimators compute the most likely explanation of the robot poses and the map given the sensor data, rather than trying to estimate the entire posterior probability. New SLAM algorithms remain an active research area, [6] and are often driven by differing requirements and assumptions about the types of maps, sensors and models as detailed ...