Adaptive Sampling For Efficient Online Modelling

This thesis examines methods enabling autonomous systems to make active sampling and planning decisions in real time. Gaussian Process (GP) regression is chosen as a framework for its non-parametric approach allowing flexibility in unknown environments. The first part of the thesis focuses on depth constrained full coverage bathymetric surveys in unknown environments. Algorithms are developed to find and follow a depth contour, modelled with a GP, and produce a depth constrained boundary. An extension to the Boustrophedon Cellular Decomposition, Discrete Monotone Polygonal Partitioning is developed allowing efficient planning for coverage within this boundary. Efficient computational methods such as incremental Cholesky updates are implemented to allow online Hyper Parameter optimisation and fitting of the GP's. This is demonstrated in simulation and the field on a platform built for the purpose. The second part of this thesis focuses on modelling the surface salinity profiles of estuarine tidal fronts. The standard GP model assumes evenly distributed noise, which does not always hold. This can be handled with Heteroscedastic noise. An efficient new method, Parametric Heteroscedastic Gaussian Process regression, is proposed. This is applied to active sample selection on stationary fronts and adaptive planning on moving fronts where a number of information theoretic methods are compared. The use of a mean function is shown to increase the accuracy of predictions whilst reducing optimisation time. These algorithms are validated in simulation. Algorithmic development is focused on efficient methods allowing deployment on platforms with constrained computational resources. Whilst the application of this thesis is Autonomous Surface Vessels, it is hoped the issues discussed and solutions provided have relevance to other applications in robotics and wider fields such as spatial statistics and machine learning in general

A Sequential Optimisation Framework for Adaptive Model Predictive Control in Robotics

State-of-the-art control and robotics challenges have long been tackled using model-based control methods like model predictive control (MPC) and reinforcement learning (RL). These methods excel in complex dynamic domains, such as manipulation tasks, but struggle with real-world issues like wear-and-tear, uncalibrated sensors, and misspecifications. These factors often perturb system dynamics, leading to the 'reality gap' problem when robots transition from simulations to real-world environments. This work aims to bridge this gap by combining RL and control in a learning framework that adapts MPC to robot decisions, optimizing performance despite uncertainties in dynamics model parameters. This thesis presents three key contributions to robotics control. The first is a novel reward-based framework for refining stochastic Model Predictive Control (MPC). It utilizes Bayesian Optimization (BO) for efficient data handling and heteroscedastic noise, linking controller hyperparameters with expected rewards through a Gaussian Process (GP). This approach demonstrates success in simulated control environments and robotic tasks. The second contribution addresses the 'reality gap' in robotics, enhancing controller performance in real-world dynamics. It builds on the first by developing an adaptive stochastic MPC that optimizes hyperparameters while estimating physical parameter distributions, employing a randomized dynamics model. This method is validated in both simulations and with robotic manipulators. Finally, the thesis proposes an innovative alternative to BO, merging it with supervised classification for a surrogate-based optimization technique. This method adeptly adjusts control hyperparameters in the face of model uncertainty and noise, optimizing complex functions through a binary classifier. Tested on simulated control problems and manipulators, it offers a promising solution to complex robotics and control challenges