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Related Concept Videos

Kinematic Equations - I01:26

Kinematic Equations - I

When an object moves with constant acceleration, the velocity of the object changes at a constant rate throughout the motion. The kinematic equations of motions are derived for such cases where the acceleration of the object is constant. The first kinematic equation gives an insight into the relationship between velocity, acceleration, and time. We can see, for example:
Kinematic Equations - II01:17

Kinematic Equations - II

The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...
Kinematic Equations - III01:18

Kinematic Equations - III

The first two kinematic equations have time as a variable, but the third kinematic equation is independent of time. This equation expresses final velocity as a function of the acceleration and distance over which it acts. The fourth kinematic equation does not have an acceleration term and provides the final position of the object at time t in terms of the initial and final velocities. This equation is useful when the value of the constant acceleration is unknown.
Using the kinematic equations,...
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
Equations of Motion: Rectangular Coordinates and Cylindrical Coordinates01:21

Equations of Motion: Rectangular Coordinates and Cylindrical Coordinates

Understanding the motion of particles is a fundamental aspect of classical mechanics, and the choice of the coordinate system plays a pivotal role in unraveling the complexities of their dynamics.
When a particle moves relative to an inertial frame, the equations of motion can be expressed using rectangular components. If the motion is confined to the x-y plane, the equations having the x and y coordinates only can be used to simplify the mathematical representation.
However, when particles...
Kinematic Equations for Rotation01:30

Kinematic Equations for Rotation

In mechanics, when one observes a rigid body in rotational motion with constant angular acceleration, it is possible to establish equations for its rotational kinematics. This process resembles how linear kinematics are dealt with in simpler motion studies.
For instance, imagine a point A on a rigid body engaged in circular motion. The translational velocity of this particular point can be calculated by taking the time derivatives of the displacement equation, which essentially measures the...

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Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms
10:32

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Published on: August 15, 2016

A note on using only position equations for robotic hand/eye calibration.

H Zhuang1

  • 1Dept. of Electr. Eng., Florida Atlantic Univ., Boca Raton, FL.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 8, 2008
PubMed
Summary
This summary is machine-generated.

This study clarifies that while the iterative robotic hand/eye calibration algorithm uses relative sensor positions, it still requires significant orientation data. Accuracy gains were not observed with added rotation equations.

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Area of Science:

  • Robotics
  • Computer Vision
  • Calibration Algorithms

Background:

  • Previous work introduced an iterative algorithm for robotic hand/eye calibration.
  • The algorithm was claimed to be less noise-sensitive and capable of using relative sensor positions.
  • Traditional linear methods often require more comprehensive sensor measurement data.

Purpose of the Study:

  • To clarify the data requirements of the iterative hand/eye calibration algorithm.
  • To address the necessity of sensor orientation information for the iterative method.
  • To explain the impact of including rotation equations on the algorithm's accuracy.

Main Methods:

  • Analysis of the iterative hand/eye calibration algorithm's data dependencies.
  • Comparison of data requirements with traditional linear calibration approaches.
  • Investigation into the effect of sensor orientation data on algorithm performance.

Main Results:

  • The iterative algorithm, despite using relative sensor positions, still necessitates substantial sensor orientation information.
  • The inclusion of rotation equations did not lead to improved accuracy in the iterative algorithm's performance.
  • The algorithm's sensitivity to noise and reliance on relative position measurements were re-evaluated.

Conclusions:

  • The iterative hand/eye calibration algorithm requires comprehensive sensor orientation data, similar to linear methods.
  • Adding rotation equations does not enhance the accuracy of this specific iterative calibration algorithm.
  • Further research may be needed to optimize iterative calibration techniques for reduced data dependency.