Reference
J. Fransman, J. Sijs, H. Dol, E. Theunissen, and B. De Schutter, "Distributed
constraint optimization for autonomous multi AUV mine counter-measures,"
Proceedings of the OCEANS 2018 Charleston, Charleston, South
Carolina, 7 pp., Oct. 2018.
Abstract
In this paper, Mine Counter-Measures (MCM) operations with multiple cooperative
Autonomous Underwater Vehicles (AUVs) are examined within the Distributed
Constraint optimization Problem (DCOP) framework. The goal of an MCM-operation
is to search for mines and mine-like objects within a predetermined area so
that ships can pass the area through a safe transit corridor. Performance
metrics, such as the expected time of completion and the level of confidence
that all mine-like objects within the area have been detected, are used to
quantify the utility of the operation. The AUVs coordinate their individual
search segments in a distributed manner in order to maximize the global
utility. The segmentation is optimized by the Compression-DPOP (C-DPOP)
algorithm, which allows explicit reasoning by the AUVs about their actions
based on the performance metrics. After initial segmentation of the mine threat
area, subsequent optimizations are triggered by the AUVs based on the
variations in sonar performance.
The performance of the C-DPOP algorithm is compared to a static segmentation
approach and validated using the high-fidelity Unmanned Underwater Vehicle
(UUV) simulation environment based on the Gazebo simulator.
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BibTeX
@inproceedings{FraSij:18-029,
author = {Fransman, Jeroen and Sijs, Joris and Dol, Henry and Theunissen,
Erik and De Schutter, Bart},
title = {Distributed Constraint Optimization for Autonomous Multi {AUV}
Mine Counter-measures},
booktitle = {Proceedings of the OCEANS 2018 Charleston},
address = {Charleston, South Carolina},
month = oct,
year = {2018}
}