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dc.contributor.authorMunnae, Jomkwunen_US
dc.date.accessioned2011-03-04T20:57:58Z
dc.date.available2011-03-04T20:57:58Z
dc.date.issued2010-11-17en_US
dc.identifier.urihttp://hdl.handle.net/1853/37219
dc.description.abstractIn visually guided control of a robot, a large residual problem occurs when the robot configuration is not in the neighborhood of the target acquisition configuration. Most existing uncalibrated visual servoing algorithms use quasi-Gauss-Newton methods which are effective for small residual problems. The solution used in this study switches between a full quasi-Newton method for large residual case and the quasi-Gauss-Newton methods for the small case. Visual servoing to handle large residual problems for tracking a moving target has not previously appeared in the literature. For large residual problems various Hessian approximations are introduced including an approximation of the entire Hessian matrix, the dynamic BFGS (DBFGS) algorithm, and two distinct approximations of the residual term, the modified BFGS (MBFGS) algorithm and the dynamic full Newton method with BFGS (DFN-BFGS) algorithm. Due to the fact that the quasi-Gauss-Newton method has the advantage of fast convergence, the quasi-Gauss-Newton step is used as the iteration is sufficiently near the desired solution. A switching algorithm combines a full quasi-Newton method and a quasi-Gauss-Newton method. Switching occurs if the image error norm is less than the switching criterion, which is heuristically selected. An adaptive forgetting factor called the dynamic adaptive forgetting factor (DAFF) is presented. The DAFF method is a heuristic scheme to determine the forgetting factor value based on the image error norm. Compared to other existing adaptive forgetting factor schemes, the DAFF method yields the best performance for both convergence time and the RMS error. Simulation results verify validity of the proposed switching algorithms with the DAFF method for large residual problems. The switching MBFGS algorithm with the DAFF method significantly improves tracking performance in the presence of noise. This work is the first successfully developed model independent, vision-guided control for large residual with capability to stably track a moving target with a robot.en_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectNonlinear least squaresen_US
dc.subjectResidual approximationen_US
dc.subjectVisual servo controlen_US
dc.subjectUncalibrated controlen_US
dc.subjectLarge residualen_US
dc.subjectBroyden estimatoren_US
dc.subjectAdaptive forgetting factoren_US
dc.subjectBFGSen_US
dc.subjectHessian approximationen_US
dc.subject.lcshRobot vision
dc.subject.lcshApproximation algorithms
dc.subject.lcshNewton-Raphson method
dc.titleUncalibrated robotic visual servo tracking for large residual problemsen_US
dc.typeDissertationen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMechanical Engineeringen_US
dc.description.advisorCommittee Chair: Lipkin, Harvey; Committee Member: Daley, Wayne; Committee Member: Ferri, Aldo A.; Committee Member: Howard, Ayanna MacCalla; Committee Member: Sadegh, Naderen_US


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