Low-Cost Embedded Vision for Industrial Robots: A Modular End-of-Arm Concept

Kroeger, Ole; Wollschlager, Felix; Krüger, Jörg

doi:10.1109/etfa46521.2020.9212093

Cited by 5 publications

(1 citation statement)

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“…Many theoretical simulation studies have been carried out on inverse kinematics applications of robot arms with different degrees of freedom [9][10][11][12][13]. On the other hand, some researchers have developed a lowcost and small-scale prototype robot arm and applied inverse kinematics results [14][15][16][17][18][19][20][21][22][23][24][25]. However, in other studies, with the emergence of Internet of Things (IoT) technology, research that allows the robot to work through internet connection has begun to be discussed.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Multi-Layer Perceptron and Inverse Kinematic for RRR Robotic Arm

et al. 2024

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In this study, the position control simulation of a 3 Degree of Freedom (3DOF) robot arm was compared with machine learning and inverse kinematic analysis separately. The considered robot arm is designed in RRR pattern. In the inverse kinematic analysis of the robot arm, the geometric approach and the analytical approach are used together. Multi-Layer Perceptron (MLP) was used as a machine learning method. Some of the coordinate data that the robot arm can reach in the working space are selected and the MLP model is trained with these data. When training was done with MLP machine learning method, the correlation coefficient (R2) was obtained as 1. Coordinates of 3 different geometric models (helix, star and daisy) that can be included in the working space are used as test data of the MLP model. These tests are simulated in 3D in MATLAB environment. The simulation results were compared with the inverse kinematics analysis data. As a result, Mean Relative Error (MRE) values for helix, star and daisy shapes were calculated as 0.0007, 0.0033 and 0.0011, respectively, in the tests performed. Mean Squared Error (MSE) values were obtained as 0.0034, 0.0065 and 0.0040, respectively. This confirms that the proposed MLP model can operate this system at the desired stability.

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