Inverse Kinematics Solution of a 6-DOF Industrial Robot

Dash, Kshitish K.; Choudhury, B. B.; Senapati, S. K.

doi:10.1007/978-981-13-0514-6_19

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…In [14], authors used optimization algorithms to improve the results of solving inverse kinematics for a 3 DOF robot. In [15], authors established an inverse kinematics solution based on ANFIS paper. ey described a three-dimensional plane manipulator as a way to maximize the effectiveness of this approach.…”

Section: Introductionmentioning

confidence: 99%

An Improved Inverse Kinematics Solution for a Robot Arm Trajectory Using Multiple Adaptive Neuro-Fuzzy Inference Systems

Refaai

2022

Advances in Materials Science and Engineering

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Inverse kinematics of robots is a critical topic in the robotics field. Although there are conventional ways of solving inverse kinematics, soft computing is an important technology that has lately gained prominence due to its ability to reduce the complexity of the inverse kinematics problem. This paper presents an inverse kinematics solution using multiple adaptive neuro-fuzzy inference systems (MANFIS). Different models were established by employing various methods of identification. Subtractive Clustering (SCM), Fuzzy C-Means Clustering (FCM), and Grid Partitioning (GP) are the three methods used in this study. This work is being carried out on a 5-DOF articulated robot arm, which is commonly used in industry. A mathematical model is built based on the Denavit-Hartenberg (DH) approach. Following confirmation that the kinematic findings of the mathematical model match the actual observed values of the robot arm, two types of data sets are generated: a random data set and a systematic data set based on a trajectory. The data sets are then utilized to train and evaluate ANFIS models and choose the optimal models to develop MANFIS model. Thus, the prediction and experimental data are compared to assess the performance of the MANFIS model.

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Section: Introductionmentioning

confidence: 99%

An Improved Inverse Kinematics Solution for a Robot Arm Trajectory Using Multiple Adaptive Neuro-Fuzzy Inference Systems

Refaai

2022

Advances in Materials Science and Engineering

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“…The proposed solution shows that ANNs have the ability to be used for kinematic determination for a simpler robotic manipulator. Dash et al 25 attempt to regress the inverse kinematic equations using the ANN. The authors conclude that the results achieved are not satisfactory, as they have a very low accuracy especially for the second and sixth joint of the manipulator, most probably caused due to a low number of training data and no hyperparameter variation.…”

Section: State-of-the-artmentioning

confidence: 99%

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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“…Dereli and Köker [13] introduced an artificial bee colony method for solving the inverse kinematics of a 7-degree-of-freedom robotic arm. Köker et al [14] introduced a three-joint robotic manipulator inverse kinematics solution based on neural networks while Dash et al [15] employed an artificial neural network to tackle a complicated inverse kinematics issue for a 6-DOF Industrial robotic manipulator. They solved the inverse kinematics using a geometric technique.…”

Section: Introductionmentioning

confidence: 99%

Surgical Self- steering Robot using Artificial Neural Network

Mahfouz,

Gamal,

Awad

et al. 2023

Preprint

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The aim of this work is to design a robot that can be used in surgery and can remove tumors by accurately and safely following contours. This is achieved by the medical imaging set such as DICOM-formatted images from several sources (CT, MRI, and Ultrasound) of these tumors using artificial neural networks. The workspace of a suggested 7 degrees of freedom robot is defined by calculating the position of the end effector for multiple positions of its links using DH tables and feed-forward kinematics. The data obtained are used to train three artificial neural networks to get the inverse kinematics. The suggested neural networks are the Generalized Regression Neural Network, Radial Basis, and Feed Forward. A training course for inverse kinematics of 23328 workspace points of the end effector of the suggested robot is used for training the three neural networks. The most accurately trained one was found to be the Feed-Forward neural network. It shows a root mean square error for those points of 2.08 mm and a training time of 1236 seconds and is to be applied to steer the robot links. The patient medical images are digitized using the image processing toolbox of MATLAB. The data obtained is to be fed to the superior neural network to get the robot links positions needed to move the end effector mounted with a cutter to follow the tumor contour accurately. The absolute error distance between the corresponding points of the contours resulting from testing the trained Feed-Forward NN with 51 points of a contour doesn’t exceed 2.08 mm.

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Inverse Kinematics Solution of a 6-DOF Industrial Robot

Cited by 7 publications

References 8 publications

An Improved Inverse Kinematics Solution for a Robot Arm Trajectory Using Multiple Adaptive Neuro-Fuzzy Inference Systems

An Improved Inverse Kinematics Solution for a Robot Arm Trajectory Using Multiple Adaptive Neuro-Fuzzy Inference Systems

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

Surgical Self- steering Robot using Artificial Neural Network

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