\begin{abstract}

    As robots become increasingly capable and robust, there is a growing scope for \emph{long-term} monitoring missions in which robots operate autonomously without human intervention.
    %
    Long-term robot missions hold immense potential across a range of scientific and industrial applications, from environmental monitoring to precision agriculture.
    %
    However, mission planning in this setting is challenging, requiring robots to reason about complex and uncertain environment models over long horizons.
    %
    Many existing works on planning for long-term robot missions use simplistic models of robots and their environments, overlooking the complexity and uncertainty inherent to real-world robotics in unstructured environments.

    This thesis addresses planning for long-term robot monitoring of unknown, continuous spatiotemporal environment processes.
    %
    We use Gaussian processes to capture the dynamics of these environment processes using sparse observational data, adopting an online approach that assumes minimal prior knowledge.
    %
    We develop planning algorithms that reason about these models in belief space over long horizons to produce effective reward-gathering policies.
    %
    Our algorithms address practical challenges such as resource constraints and uncertainty over action durations, which are fundamental to long-term robot missions in the real world.
    %
    % We also present an algorithm for safe exploration and mapping in the presence of an unknown environmental hazard, extending the scope of our monitoring work to environments in which a prior map is not available.
    We also present an algorithm for safe exploration and mapping in the presence of an unknown environmental hazard, enabling a robot to autonomously and safely map an unknown environment in which no prior map is available.
    %
    Our empirical results demonstrate that our planning solutions are able to generate effective policies in realistic and challenging long-term robot monitoring scenarios.

\end{abstract}

