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\wikidoc{file=Proj.Prodromos}
\wikidoc{auth=mikerb@mit.edu}

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\section*{Prodromos - USV Collision Avoidance from ML-Based Perception}
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A core function Unmanned Surface Vehicle (USV) autonomy is the ability
to safely avoid other vessels. The collision avoidance strategy is
only as good as the information about the location and trajectories of
nearby vessel traffic. The goal of {\em Prodromos} is to enhance USV
collision avoidance capabilities by using Machine Learning (ML)
generated perception capabilities derived from rich datasets collected
on the Charles River at MIT. The project has three components:

\begin{packed_itemize}
\item {\em Datasets}: The collection of rich datasets of sailboat
  traffic on the Charles River. During each dataset collection, a
  number of MIT sailboats will be fitted with a small computer box
  with GPS, logging ground-truth position, heading and speed of the
  sailboat. One box per sailboat, for {\em all} participating
  boats. The MIT autonomous Boston Whaler, Philos, will operate nearby
  collecting camera, LIDAR, and radar data of all sailboats within
  sensor range. Each dataset will be comprised of the time-synchronized
  data streams and logged data from each sailboat.
\item {\em Machine Learning}: The datasets will be used to train a ML
  system with the goal of achieving a trained system capable of
  providing contact position, heading and speed from live sensor data
  in a deployed USV such as Philos.
\item {\em Autonomy}: MIT collision avoidance behaviors will use the
  output of the ML-based perception system to generate autonomy decisions
  for safely avoiding contacts in the field.
\end{packed_itemize}

\noindent
The end demonstration goal of Prodromos is to deploy the MIT USV,
Philos, to autonomously traverse from Boston to Cambridge through
sailboat traffic in a consistently reliable and safe manner.

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

  \var{Status}: &amp; Ongoing since May 2021 \\ \hline
  \var{Sponsors}: &amp; Brunswick (Philos USV)  \\ \hline
  \var{People}: &amp; Mike Sacarny, Mike DeFilippo, Mike Benjamin, Maaya Prasad \\ \hline
  \var{Robots:} &amp; \urlx{https://oceanai.mit.edu/pavlab/robots/philos} \\ \hline
\end{longtable}
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%\section*{Relevant Prior Publications}

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