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

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\section*{Proposal Based Adaptive Channel Search (PBACS)}
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This project addresses the problem of quickly finding a deep channel
in a body of water using multiple unmanned vehicles.  Our approach
uses a gaussian process regression to build a non-parametric model of
the bathymetry.  These models are fused using a decentralized modified
Kalman consensus algorithm which operates intermittently, and does not
require a fully connected network graph to converge.  Actions for each
vehicle in the group are allocated using a new proposal and auction
algorithm PBACS, which directs the search along paths that may still
be viable.  All of these techniques are completely decentralized, and
the inherent robustness is critical as we find communication dropouts
can happen often in the field.



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

  \var{Status}: &amp; Ongoing since Summer 2021 \\ \hline
  \var{Sponsor(s)}: &amp; United States Military Academy at
  West Point and the Office of the Undersecretary of Defense
  for Research and Engineering (ACC-APG-RTP W911NF2120206) \\ \hline
  \var{People}: &amp; Tyler Paine, Nick Gershfeld, Mike Benjamin (PI) \\ \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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