On-line Mobile Robotic Dynamic Modeling using Integrated Perturbative Dynamics

摘要

Mobile robotic dynamics modeling is necessary for reliable planning and control of unmanned ground vehicles on rough terrain. Autonomous vehicle research has continuously demonstrated that a platform's precise understanding of its own mobility is a key ingredient of competent machines with high performance. I will investigate the feasibility and mechanism of enabling a platform to better predict its own mobility by learning from its own experience. The autonomy system will calibrate, in real-time, vehicle dynamics models, based on residual differences between the motion originally predicted by the platform and the motion ultimately experienced by the platform. This thesis develops an integrated perturbative dynamics method for real-time identification of wheel-terrain interaction models for enhanced autonomous vehicle mobility. I develop perturbative dynamics model which predict vehicle slip rates. The slip rates are first learned for steady state conditions and interpolated to slip rate surfaces. An Extended Kalman Filter uses the residual pose differences for on-line identification of the perturbative parameters on a six wheel, skid steered vehicle. An order of magnitude change in relative pose prediction was observed on loose and muddy gravel.