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Related Concept Videos

Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

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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...
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Kinematic Equations - II01:17

Kinematic Equations - II

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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...
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Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
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Kinematic Equations - III01:18

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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,...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
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Kinematic Equations - I01:26

Kinematic Equations - I

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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:
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Updated: Sep 20, 2025

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms
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A Tandem Robotic Arm Inverse Kinematic Solution Based on an Improved Particle Swarm Algorithm.

Guojun Zhao1,2, Du Jiang1,3, Xin Liu1,2

  • 1Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China.

Frontiers in Bioengineering and Biotechnology
|June 6, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Particle Swarm Algorithm (PSO) for robot inverse kinematics, enhancing accuracy and speed. The novel approach offers superior performance in robot control and path planning applications.

Keywords:
adaptive strategyjoint limitingparticle swarm algorithmrobot inverse kinematics solutionspinor theory

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Mechanics

Background:

  • Robot inverse kinematics is crucial for control and path planning, with traditional methods facing limitations.
  • Intelligent algorithms offer advantages by directly solving forward kinematics equations, reducing computational steps.
  • Particle Swarm Algorithm (PSO) is a widely used intelligent algorithm known for its simplicity and performance.

Purpose of the Study:

  • To propose an improved Particle Swarm Algorithm (PSO) for solving robot inverse kinematics problems.
  • To enhance the search ability of PSO through adaptive weight adjustment and modified velocity factors.
  • To utilize an exponential product form (POE) modeling method based on spinor theory for improved kinematic description.

Main Methods:

  • Developed an adaptive weight adjustment strategy for PSO to improve global and local search capabilities.
  • Introduced a condition setting based on limit joints and a position coefficient k in the velocity factor to optimize running time.
  • Employed the exponential product form (POE) modeling method based on spinor theory, contrasting it with the traditional Denavit-Hartenberg (DH) method.

Main Results:

  • The improved PSO algorithm demonstrated superior accuracy in position and orientation compared to traditional PSO and Quantum PSO (QPSO).
  • Achieved near-zero position error (0) and minimal orientation error (1.29 × 10⁻⁸) for the proposed algorithm.
  • Outperformed other algorithms in computation time, with faster and more stable convergence observed across different robotic arm models.

Conclusions:

  • The proposed improved PSO algorithm offers significant advantages in accuracy, speed, and convergence for robot inverse kinematics.
  • The spinor-based POE modeling method effectively avoids singularities associated with local coordinate systems.
  • The algorithm shows high applicability and potential for solving complex multi-arm inverse kinematics problems.