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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Mechanism Design IV

ISBN

978-3-031-70250-1

ISSN

2211-0984

e-ISSN

—

Počet stran výsledku

10

Strana od-do

202-211

Název nakladatele

Springer Science+Business Media B.V.

Místo vydání

Dordrecht

Místo konání akce

Liberec

Datum konání akce

3. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001328769400022