Cooperative Route Planning Framework for Multiple Distributed Assets in Maritime Applications.

Senjuti Basu Roy

Abstract:

This work formalizes the Route Planning Problem (RPP), wherein a
set of distributed assets (e.g., ships, submarines, unmanned systems)
simultaneously plan routes to optimize a team goal (e.g., find the lo-
cation of an unknown threat or object in minimum time and/or fuel
consumption) while ensuring that the planned routes satisfy certain
constraints (e.g., avoiding collisions and obstacles). This problem
becomes overwhelmingly complex for multiple distributed assets as
the search space grows exponentially to design such plans. The RPP
is formalized as a Team Discrete Markov Decision Process (TDMDP)
and we propose a Multi-agent Multi-objective Reinforcement Learn-
ing (MaMoRL) framework for solving it. We investigate challenges
in deploying the solution in real-world settings and study approx-
imation opportunities. We experimentally demonstrate MaMoRL’s
effectiveness on multiple real-world and synthetic grids, as well
as for transfer learning. MaMoRL is deployed for use by the Naval
Research Laboratory - Marine Meteorology Division (NRL-MMD),
Monterey, CA.