All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Accuracy of the Inverse Kinematics of a Planar Redundant Manipulator Solved by an MLP Neural Network

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F24%3A00377356" target="_blank" >RIV/68407700:21220/24:00377356 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/978-3-031-70251-8_22" target="_blank" >https://doi.org/10.1007/978-3-031-70251-8_22</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-031-70251-8_22" target="_blank" >10.1007/978-3-031-70251-8_22</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Accuracy of the Inverse Kinematics of a Planar Redundant Manipulator Solved by an MLP Neural Network

  • Original language description

    The inverse kinematics of manipulators can be solved by approximating the data obtained using forward kinematics through the use of a multi-layer perceptron neural network, provided the input and output data are swapped before training. However, with redundant manipulators, reaching the same point is possible with an infinite number of joint angle settings, rendering the existence of an inverse function impossible. Data for training the neural network must be prepared so that only one combination of angles is used for each point reached. Achieving this may involve supplementing the obstacle avoidance function, even though the forward kinematics of the manipulator lacks a clear, analytically accessible solution. The paper addresses how to find a solution to forward kinematics that optimally fulfills the function describing the obstacle avoidance problem, ensuring uniqueness and continuity for use in neural network training. The acquired data were used to train a neural network with one hidden layer, and the accuracy of the resulting network was verified on a simple trajectory.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20204 - Robotics and automatic control

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2024

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Advances in Mechanism Design IV

  • ISBN

    978-3-031-70250-1

  • ISSN

    2211-0984

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    202-211

  • Publisher name

    Springer Science+Business Media B.V.

  • Place of publication

    Dordrecht

  • Event location

    Liberec

  • Event date

    Sep 3, 2024

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    001328769400022