A Precise Robotic Arm Positioning Using an SVM Classification Algorithm

A Precise Robotic Arm Positioning Using an SVM Classification Algorithm

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The present thesis presents an approach for complete characterization of robot poses in the search of auto positioning for grasping objects. A vector of parameters for describing the robot poses and computer vision using Principal Component Analysis (PCA) for auto analysis of positioning are adopted as well as a manual characterization of robotic joints. The vectors resulting from the manual and auto characterization of robot poses are integrated with an SVM (Support Vector Machines) classification algorithm to discriminate robot poses. The results from the research are encouraging and show that the robotic positioning in interaction with object detection can be achieved by combining meticulous recognition techniques and evolutionary classification techniques.5.2.4 Evaluation Just like the manual case, in the second scenario, the dataset is labeled beforehand and then compare it ... classification discriminant I13 I15 I12 I14 I9 I16 I10 I11 I4 I6 I7 I1 I8 I2 I3 I5 I19 I18 I23 I17 I20 I21 I22 I24 I43 I41 I42 I28 anbsp;...


Title:A Precise Robotic Arm Positioning Using an SVM Classification Algorithm
Author: Michael Terrones
Publisher:ProQuest - 2007
ISBN-13:

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