Learning based Control for Autonomous Mobile Robots
Learning based Control for Autonomous Mobile Robots Christopher John Ostafew Doctor of Philosophy Graduate Department of Aerospace Science and Engineering University of Toronto 2016 As mobile robots leave structured indoor environments to operate in challenging outdoor environments, their motion controllers require advanced techniques to mitigate the effects of unmodelled surface materials (e.g. snow, sand, grass) terrain topography (e.g. side slopes, inclines) and complex robot dynamics. This thesis investigates learning based control within the context of path tracking autonomous mobile robots. Learning based algorithms alleviate the need for significant engineering work in identifying and modelling all disturbances that a controller may be required to mitigate. Furthermore, they are capable of predicting and acting in anticipation of repeatable effects and disturbances not modelled prior to deployment. The learning based algorithms reduce tracking errors, increase operational speed, and increase localization reliability. Specifically, the thesis presents four approaches: 1) Iterative Learning Control (ILC) 2) Learning based Nonlinear Model Predictive Control (LB NMPC) 3) Robust Min Max LB NMPC (MM LB NMPC) and 4) Robust Constrained LB NMPC (RC LB NMPC) ILC generates an acausal feedforward signal that reduces the path tracking errors using information from any previous trial. While the approach is computationally appealing, ILC typically assumes that the robot is initialized with identical initial conditions for each trial and tracking the same desired path (i.e. generalization is non trivial) On the other hand, LB NMPC is a technique that uses a learned process model directly, enabling interpolation and extrapolation from experiences. In this case, the current control action is obtained by solving a finite horizon optimal control problem using the current