Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

12.7K
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...
12.7K
Kinematic Equations for Rotation01:30

Kinematic Equations for Rotation

368
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...
368
Kinematic Equations - III01:18

Kinematic Equations - III

8.1K
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,...
8.1K
Kinematic Equations - II01:17

Kinematic Equations - II

10.2K
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...
10.2K
Kinematic Equations - I01:26

Kinematic Equations - I

11.5K
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:
11.5K
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

798
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...
798

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

PATZ1 condensation adjacent to PML nuclear bodies suppresses HBoV transcription as an intrinsic antiviral defense.

Cell reports·2026
Same author

Real-Time Seam Extraction Using Laser Vision Sensing: Hybrid Approach with Dynamic ROI and Optimized RANSAC.

Sensors (Basel, Switzerland)·2025
Same author

The impact of Internet use on workers' job satisfaction: Heterogeneous and mediating analyses.

Work (Reading, Mass.)·2025
Same author

N4-acetylcytidine coordinates with NP1 and CPSF5 to facilitate alternative RNA processing during the replication of minute virus of canines.

Nucleic acids research·2025
Same author

Characterization of the Pathogenic Features of Multiple SARS-CoV-2 Pandemic Strains in Different Mouse Models.

Journal of medical virology·2024
Same author

Characterization of ACTN4 as a novel antiviral target against SARS-CoV-2.

Signal transduction and targeted therapy·2024

Related Experiment Video

Updated: Sep 3, 2025

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

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

Published on: August 15, 2016

15.6K

A Novel Hybrid Algorithm for the Forward Kinematics Problem of 6 DOF Based on Neural Networks.

Huizhi Zhu1, Wenxia Xu1, Baocheng Yu1

  • 1Engineering Research Center for Intelligent Production Line Equipment of Hubei Province, School of Computer Science and Engineering Artificial Intelligence, Wuhan Institute of Technology, Wuhan 430205, China.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

This study introduces a novel hybrid algorithm for Gough-Stewart platform forward kinematics. It enhances accuracy and overcomes limitations of traditional methods, improving computational efficiency.

Keywords:
ABC–BPNNGough–StewartNewton’s methodforward kinematics problem

More Related Videos

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

9.6K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K

Related Experiment Videos

Last Updated: Sep 3, 2025

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

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

Published on: August 15, 2016

15.6K
A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

9.6K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K

Area of Science:

  • Robotics
  • Computational Mechanics
  • Artificial Intelligence

Background:

  • Gough-Stewart platforms present kinematic control challenges, especially forward kinematics, due to their closed structure.
  • Traditional hybrid algorithms (BPNN and Newton-Raphson) suffer from neural network training difficulties and Newton-Raphson's inability to handle singular Jacobian matrices.

Purpose of the Study:

  • To develop a robust hybrid algorithm for solving the forward kinematics problem of Gough-Stewart platforms.
  • To enhance the accuracy and efficiency of kinematic solutions by addressing limitations of existing methods.

Main Methods:

  • A novel hybrid algorithm combining an Artificial Bee Colony (ABC)-optimized Backpropagation Neural Network (ABC-BPNN) with a modified numerical algorithm (QMn-M).
  • ABC optimizes the BPNN for improved prediction and provides initial values for numerical methods.
  • QMn-M is introduced to resolve issues with singular matrices encountered in traditional numerical algorithms.

Main Results:

  • The ABC-BPNN achieved a maximal error optimization improvement of 46.3% and a 42.1% decrease in Root Mean Square Error (RMSE).
  • The QMn-M algorithm demonstrated feasibility in solving singular matrix problems.
  • The new hybrid approach showed a 14.4% improvement in average iterations and a 13.9% reduction in computation time.

Conclusions:

  • The proposed ABC-BPNN and QMn-M hybrid algorithm effectively solves the forward kinematics problem for Gough-Stewart platforms.
  • This method significantly improves prediction accuracy and computational efficiency compared to traditional approaches.
  • The study highlights the potential of AI-driven optimization and modified numerical methods in complex robotic kinematic analysis.