Improvement of vision guided robotic accuracy using Kalman filter

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.

► A novel approach for quality control (e-quality for manufacture: EQM), is devised and tested.
► In EQM, vision calibration requires a complex mathematical modeling, yet unable to counter inherent noises and variations.
► Using the Kalman filter, this study effectively reduces the positioning inaccuracies in vision calibration.
► The API allows the remotely located operators to monitor and control the robot’s positioning accuracy over the network.
► The EQM enables proactive and timely actions to be taken prior to the production of any defects.