In this paper, we introduce an approach for decomposing exploration tasks among multiple Unmanned Surface Vehicles (USVs) in port regions. In order to ensure effective distribution of the workload, the algorithm has to consider the effects of the environment on the physical constraints of the USVs. The performance of the USV is influenced by the surface currents, risk of collision with the civilian traffic, and varying depths as a result of tides, and weather. In our approach, we want the team of USVs to explore certain region of the harbor. The algorithm has to decompose the region of interest into multiple sub-regions by considering the maximum operating velocity of each USV in the given environmental conditions. The algorithm overlays a 2D grid upon a given map to convert it to an occupancy grid, and then proceeds to partition the region of interest among the multiple USVs assigned to explore the region. During partitioning, each USV covers the maximum area that is possible by operating at maximum velocity at each time-step. The objective is to minimize the time taken for the last USV to finish claiming its area exploration. We use the particle swarm optimization (PSO) method to compute the optimal region partitions. The method is verified by running simulations in different test environments. We also analyze the performance of the developed method in environments with unknown velocity profiles.

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