\chapter{Conclusion}
\label{ch:conclusion}
\minitoc

\section{Summary of contributions}

This thesis has contributed planning algorithms for long-term robot monitoring missions in complex and uncertain environments, geared towards applications such as ecological monitoring and precision agriculture.
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In doing so, we have addressed a number of the challenges that arise when planning in this context.
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Here, we summarise the contributions of our work to addressing the four challenges outlined in Section~\ref{sec:challenges}.


\paragraph{\challengeref{challenge:modelling}}
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In Chapter~\ref{ch:LTM1}, we formalised the problem of long-term monitoring for robots over multiple deployments as a \pomdpfull{}.
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We introduced a belief-based MCTS planning approach for this setting, using a GP model of an unknown spatiotemporal environment process.
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We demonstrated that, with a well-chosen GP kernel, such a model can enable planning in this setting, making effective use of the limited and noisy data available to the robot.
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Our planning approach incorporates a routine for learning GP hyperparameters online between missions, allowing the robot to adapt its model to environments about which it has little prior knowledge.
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Further, we employed a novel batch evaluation method for GP-based rewards in MCTS, enabling faster rollouts to mitigate the high computational demand of trial-based planning using GPs.
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In Chapter~\ref{ch:LTM2}, we scaled up our planning capabilities using a hierarchical approach, and demonstrated that our GP framework remains viable in larger domains.
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Throughout this thesis, we used GPs to model bioactivity in coral reefs, wildfire hotspots, and gamma radiation fields.
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The diversity of these applications demonstrates the versatility of GP models for planning in diverse environments.




\paragraph{\challengeref{challenge:resources}}
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In Chapter~\ref{ch:LTM2}, we addressed planning for long-term robot monitoring missions under resource constraints, where the robot was able to replenish its resources at depots to enable uninterrupted autonomous operation.
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We introduced a hierarchical approach to this planning problem, where the high-level planner handled resource management and goal selection, and the low-level planner handled action selection to execute the chosen goals.
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We demonstrated empirically that the flexibility of allowing a robot to manage its own resources was beneficial to its mission outcomes, allowing it to adapt its active hours based on when the most reward could be gained.
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We also saw that the hierarchical approach enabled effective planning over longer horizons, and that---with some additional computational cost---this enabled the robot to achieve better mission outcomes.
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In Chapter~\ref{ch:LTM3}, we also integrated a more realistic robot battery model into our planner, in order to better reflect the constraints of real-world operations.


\paragraph{\challengeref{challenge:stochastic}}
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The dynamics of mobile robots operating in unstructured environments are complex, which can make it difficult to accurately model the outcomes of actions.
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In Chapter~\ref{ch:LTM3}, we extended the work from Chapter~\ref{ch:LTM2} to consider stochastic models of action durations and resource replenishment.
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We developed a chance-constrained planning approach, demonstrating that principled consideration of uncertainty could enable robust planning in the face of stochastic dynamics.
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Empirically, we showed that explicit consideration of uncertainty allowed our planner to uphold the chance constraint while still achieving comparable reward to a baseline planner.
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In environments with higher degrees of stochasticity, we showed that explicitly incorporating uncertainty into planning became increasingly important.
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\paragraph{\challengeref{challenge:priors}}
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In Chapter~\ref{ch:safe_exp}, we addressed the challenge of safe robot exploration and mapping of environments with unknown hazards.
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This is an essential capability to enable robot deployments in new environments, removing the need for prior knowledge of the environment, which can be costly or difficult to obtain.
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We presented the SafeEstMDP-Map algorithm, which enables a robot to autonomously map the full safely reachable region of an environment, while remaining safe with respect to some GP-modelled hazard.
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The output of this exploration is a topological map of the safely reachable area, which can be used for subsequent deployments in the same environment.
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We demonstrated empirically in simulation that our approach can enable effective exploration in the presence of hazardous radiation, striking a balance between exploration and safety.
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Additionally, in Chapters~\ref{ch:LTM1}, \ref{ch:LTM2}, and \ref{ch:LTM3}, we  integrated GP hyperparameter learning into our planning algorithms, making them more adaptable and reducing their reliance on prior knowledge of the environment features of interest.

\section{Future work}
\label{sec:future_work}

The work presented in this thesis opens up a variety of avenues for future research into planning for long-term robot monitoring.
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Although we have addressed each of the challenges outlined in Section~\ref{sec:challenges}, there are still many directions in which the boundaries of each could be pushed further.
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We outline some of the more promising ones below.

\paragraph*{Scaling Gaussian processes}

Throughout this thesis, we used GPs to model continuous environment processes based on noisy observations.
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One limitation of our approach is that both the memory required for storing the dataset, and the computational demand of GP inference, grow without bound as the size of the dataset increases.
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While this did not pose a problem for the experiments presented in this thesis, it does in principle limit the scalability of our approach.
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As discussed in Section~\ref{ssec:gp_scaling}, techniques such as sparse approximations and windowing could be used to mitigate this issue, allowing for a limit to be placed on the allowable size of the dataset used for GP inference.
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% This would also be the main missing piece required to bridge the gap between long-term and persistent monitoring. 
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With a mechanism to cap the memory and computation demands of GP inference, the planner presented in Chapter~\ref{ch:LTM3} would be able to operate indefinitely, enabling true persistent monitoring.
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A study into the effectiveness of these techniques in the context of long-term monitoring performance over time would be a valuable extension to our work.

\paragraph*{Stochastic transition models}

In Chapter~\ref{ch:LTM3}, we introduced a stochastic model of action durations, in order to better reflect the complexity of robot dynamics in  the real world.
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This meant that the outcomes of actions were described by distributions, rather than fixed values.
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However, we still assumed that actions were guaranteed to produce the intended result when executed, which is often a limiting assumption in robotics applications.
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An AUV navigating underwater, for example, might be swept off course by a strong current, ending up in a different location entirely than the one it intended to reach~\cite{stephens2024auro}.
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Or, a ground robot might find a path unexpectedly blocked by an obstacle, resulting in a failed action.
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Incorporating these outcomes into the probabilistic transition function would allow the planner to reason about them explicitly, resulting in more robust performance in challenging environments.
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Moreover, the transition function could be learned online, allowing the robot to adapt to changes in its environment over time.


\paragraph*{Multi-robot monitoring}

Much of the literature on long-term monitoring, surveillance and coverage planning focuses on multi-robot teams, since teams can more easily cover large environments.
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The work presented in this thesis has focused on addressing the single-robot problem, and extending this work to multi-robot teams represents a natural next step.
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This would involve addressing several additional challenges, such as decentralised communication, coordination, and task allocation.
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However, in doing so, it could enable operation at much larger scales, and with an additional layer of redundancy to mitigate the costs associated with failure of a single robot.



% Mobile depots?
% Our high-level model could also be extended to scale better to a broader range of timescales, by planning in smaller steps for the near future and larger steps farther out.

% \section{Concluding remarks}

% \note{Overall conclusion}