|SUBJECT:||M.S. Thesis Presentation|
|TIME:||Wednesday, April 28, 2021, 11:00 a.m.|
|TITLE:||Ultra-wideband Localization on Manifolds for Autonomous Metal Structure Inspection|
|COMMITTEE:||Dr. Nico Declercq, Co-Chair (ME)
Dr. Cédric Pradalier, Co-Chair (CS)
Dr. Jonathan Rogers (ME/AE)
A robot that can probabalistically infer its state and uncertainties while exploiting differential geometry is capable of achieving more consistent, more accurate, robust state estimation. It is being proposed that ultra-wideband, a cutting-edge technology, that is also highly unpredictable, can be used to give autonomy to a magnetic-wheeled crawler robot for the application of metal structure inspection. Thus, ultra-wideband technology is evaluated based on its sensitivity to metal surfaces at varying heights, as well as its response to varying grid sizes between receivers, featuring experiments with a Turtlebot and an RTK-GPS. Then, a novel methodolgy for ultra-wideband grid initialization is presented featuring a simulation of a ship hull with an ultra-wideband grid. Finally, by considering a metal structure as a parallelizable manifold with a bivariate b-spline representation, and by applying the matrix exponential correspondence between a Lie group and its Lie algebra for the Special Orthogonal Group within the Kalman filter framework, the Manifold Invariant Extended Kalman Filter (M-IEKF), a novel approach to more robust state estimation is derived, presented, and evaluated. It is evaluated in comparison with a modified standard approach, the Manifold-Constrained Extended Kalman Filter (MC-EKF). Then, for a real proof of concept, an experiment using a real magnetic-wheeled crawler robot with ultra-wideband localization on a curved metal surface is carried out, showing viability of the approach in the application of autonomous metal structure inspection.