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Kinematic diagram of Cartesian (coordinate) robot A plotter is a type of Cartesian coordinate robot.. A Cartesian coordinate robot (also called linear robot) is an industrial robot whose three principal axes of control are linear (i.e. they move in a straight line rather than rotate) and are at right angles to each other. [1]
A polar plotter also known as polargraph or Kritzler is a plotter which uses two-center bipolar coordinates to produce vector drawings using a pen suspended from strings connected to two pulleys at the top of the plotting surface. This gives it two degrees of freedom and allows it to scale to fairly large drawings simply by moving the motors ...
English: Robotron calculator in the Museum of historical vehicles, agricultural technology and crafts in Pořežany, České Budějovice District, the Czech Republic. Čeština: Kalkulačka v Muzeu historických vozidel, zemědělské techniky a řemesel v Pořežanech, okres České Budějovice.
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The radius and the azimuth are together called the polar coordinates, as they correspond to a two-dimensional polar coordinate system in the plane through the point, parallel to the reference plane. The third coordinate may be called the height or altitude (if the reference plane is considered horizontal), longitudinal position , [ 1 ] or axial ...
Interactive geometry software (IGS) or dynamic geometry environments (DGEs) are computer programs which allow one to create and then manipulate geometric constructions, primarily in plane geometry. In most IGS, one starts construction by putting a few points and using them to define new objects such as lines , circles or other points.
In robotics, Vector Field Histogram (VFH) is a real time motion planning algorithm proposed by Johann Borenstein and Yoram Koren in 1991. [1] The VFH utilizes a statistical representation of the robot's environment through the so-called histogram grid, and therefore places great emphasis on dealing with uncertainty from sensor and modeling errors.
For that, the tool we want is the polar decomposition (Fan & Hoffman 1955; Higham 1989). To measure closeness, we may use any matrix norm invariant under orthogonal transformations. A convenient choice is the Frobenius norm , ‖ Q − M ‖ F , squared, which is the sum of the squares of the element differences.