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, 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.