Cooperative Route Planning Framework for Multiple Distributed Assets in Maritime Applications

Document Type

Conference Proceeding

Publication Date

6-11-2022

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 location 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 Learning (MaMoRL) framework for solving it. We investigate challenges in deploying the solution in real-world settings and study approximation 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.

Identifier

85132707036 (Scopus)

ISBN

[9781450392495]

Publication Title

Proceedings of the ACM SIGMOD International Conference on Management of Data

External Full Text Location

https://doi.org/10.1145/3514221.3526131

ISSN

07308078

First Page

1518

Last Page

1527

Grant

1814595

Fund Ref

National Science Foundation

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