On the Number of Training Samples for Inverse Kinematics Solutions by Artificial Neural Networks

Lim, Dong-Won; Lee, Yong-Kwun

doi:10.1109/urai.2019.8768773

Cited by 1 publication

(2 citation statements)

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“…Multiple algorithms are compared by authors (quantum behaved particle swarm, firefly algorithm, particle swarm optimization, and artificial bee colony) and best results are shown when using quantum behaved particle swarm. Lim and Lee 29 discuss the number of points necessary for the inverse kinematic solutions. Their findings show that regression can be learned with as little as 125 experimentally obtained data points.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Doing this yields a dataset of 15,000 data points, which is a large enough amount to attempt a regression analysis using artificial intelligence techniques. 29 Furthermore, analysis of the values in the dataset shows that all of the input and output values are unique, allowing for unique mapping from the inputs to outputs. While this dataset could have been generated experimentally, through robot positioning and measurement of joint angles, this would have taken an extremely long time and would have added the problem of measurement imprecision and errors.…”

Section: Dataset Generationmentioning

confidence: 99%

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Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator

Šegota

Anđelić

Mrzljak

et al. 2021

International Journal of Advanced Robotic Systems

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Inverse kinematic equations allow the determination of the joint angles necessary for the robotic manipulator to place a tool into a predefined position. Determining this equation is vital but a complex work. In this article, an artificial neural network, more specifically, a feed-forward type, multilayer perceptron (MLP), is trained, so that it could be used to calculate the inverse kinematics for a robotic manipulator. First, direct kinematics of a robotic manipulator are determined using Denavit–Hartenberg method and a dataset of 15,000 points is generated using the calculated homogenous transformation matrices. Following that, multiple MLPs are trained with 10,240 different hyperparameter combinations to find the best. Each trained MLP is evaluated using the R 2 and mean absolute error metrics and the architectures of the MLPs that achieved the best results are presented. Results show a successful regression for the first five joints (percentage error being less than 0.1%) but a comparatively poor regression for the final joint due to the configuration of the robotic manipulator.

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Section: State-of-the-artmentioning

confidence: 99%

Section: Dataset Generationmentioning

confidence: 99%