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\begin{document}

\wikidoc{file=Proj.PopAuto}
\wikidoc{auth=tpaine@mit.edu}

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\section*{Census-based population autonomy}
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The goal of this research effort is to develop a comprehensive model
of decentralized multi-agent autonomy that can express group consensus
and dissensus over multiple options, heterogeneous individual
behaviors that are multi-objective, and action execution via modern
control analysis.  This model is applied to several multi-agent
scenarios and field tested using the fleet of Heron USVs at MIT and
the fleet of SeaRobotics SR surveyor M1.8 operated by our
collaborators at USMA West Point.  We also collaborate on theoretical
topics with Professor Bizyaeva at Cornell University.


\begin{figure}[ht]
  \centering 
  \includegraphics[width=0.95\textwidth]{figures/popauto1.png}
  \label{fig_popauto1}
  \caption{Left to Right: Model overview, Multi-agent deployments at
    night in Charles River, MA, Multi-agent deployments in Lake
    Popolopen, NY.}
\end{figure}

\vspace{0.1in}
\noindent
The work on this model is on-going, but we have published preliminary
work that shows it is possible, under certain conditions, to use the
natural excitation properties of this model to adaptively estimate
parameters of an unknown collective reward function and thus
adaptively control a key parameter that influences decision-making.

\vspace{0.1in}
\begin{figure}[ht]
  \centering 
  \includegraphics[width=0.85\textwidth]{figures/popauto2.png}
\label{fig_popauto2}
\caption{Numerical simulation that confirms numerical analysis of
  adaptive estimation and control for multi-agent groups operating in
  the dissensus mode.}
\end{figure}

\vspace{0.1in}
\noindent
We find the model generalizes well to many different types of
scenarios, and can be reduced to recover many other algorithms for
distributed optimization.  This model significantly extends previous
work through the inclusion of heterogeneous behaviors among agents and
allocation via the concept of dissensus.  In field experiments, we
find the approach is robust to time-varying network topology including
dropout. This work is ongoing.

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%\section*{Quick Facts}
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\vspace{0.15in}
\footnotesize
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\begin{longtable}{|r|p{0.76\textwidth}|} \hline

  \var{Status}: &amp; Ongoing since February 2023 \\ \hline
%  \var{Sponsor(s)}: &amp; DARPA I20, Dr. John-Francis Mergen. Aurora Flight Sciences. \\ \hline
  \var{People}: &amp; Tyler Paine \\ \hline
  \var{Robots}: &amp;  \urlx{https://oceanai.mit.edu/pavlab/herons} \\ \hline
  \var{Software:} &amp; MOOS-IvP public codebase, MOOS-IvP-Pavlab codebase \\ \hline
%  \var{Photos:} &amp; \urlx{https://oceanai.mit.edu/media/WestPoint2022/album} \\ \hline
\end{longtable}
\normalsize


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\section*{Recent Publications}

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