Distributed constraint optimization for autonomous multi AUV mine counter-measures

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 quantity 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.