On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
On Optimal Camera Parameter Selection in Kalman Filter Based Object Tracking
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Self-calibrated robotic manipulator force observer
Robotics and Computer-Integrated Manufacturing
Reliable people tracking approach for mobile robot in indoor environments
Robotics and Computer-Integrated Manufacturing
Autonomous navigation of an automated guided vehicle in industrial environments
Robotics and Computer-Integrated Manufacturing
Enhancing e-quality for manufacture using Kalman Filter calibrated visual robotic control
Robotics and Computer-Integrated Manufacturing
E-Quality for Manufacturing (EQM) Within the Framework of Internet-Based Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This study addresses one of the most prevalent, yet difficult problems in vision calibration, namely, the accuracy of remote, vision-guided robotic control in tune with the notion of e-quality for manufacture (EQM). In all areas of robot work space, image distortion occurs due to imperfect lens curvatures, which in turn induces an inaccurate vision guidance. Non-uniform nature of image distortion is effectively rectified, using the Kalman Filtering technique. Consequently, the robotic positioning accuracy is significantly improved. In recent years, stringent quality standards and intense competition compelled many companies to adopt new, advanced technologies to further enhance their strategic competitiveness. EQM is an emergent technology better suited for today's fast-changing, zero-defect production environment. The proposed methodology has great potential for improving the product quality and operational efficiency of networked robotic production system, which has been vindicated by the statistical analysis.