Design Innovation Paper

Decomposition of Collaborative Surveillance Tasks for Execution in Marine Environments by a Team of Unmanned Surface Vehicles

[+] Author and Article Information
Shaurya Shriyam, Brual C. Shah

Department of Aerospace & Mechanical Engineering,
University of Southern California,
Los Angeles, CA 90089

Satyandra K. Gupta

Department of Aerospace & Mechanical Engineering,
University of Southern California,
Los Angeles, CA 90089
e-mail: guptask@usc.edu

1Corresponding author.

Manuscript received September 26, 2017; final manuscript received December 14, 2017; published online February 12, 2018. Assoc. Editor: Venkat Krovi.

J. Mechanisms Robotics 10(2), 025007 (Feb 12, 2018) (7 pages) Paper No: JMR-17-1329; doi: 10.1115/1.4038974 History: Received September 26, 2017; Revised December 14, 2017

This paper introduces an approach for decomposing exploration tasks among multiple unmanned surface vehicles (USVs) in congested regions. In order to ensure effective distribution of the workload, the algorithm has to consider the effects of the environmental constraints on the USVs. The performance of a USV is influenced by the surface currents, risk of collision with the civilian traffic, and varying depths due to tides and weather. The team of USVs needs to explore a certain region of the harbor and we need to develop an algorithm to decompose the region of interest into multiple subregions. The algorithm overlays a two-dimensional 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, the rate at which each USV is able to travel varies with the applicable speed limits at the location. The objective is to minimize the time taken for the last USV to finish exploring the assigned area. 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 where speed restrictions are not known in advance.

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Grahic Jump Location
Fig. 1

This is an illustration of a port scenario where a region has been demarcated for exploration by multiple USVs

Grahic Jump Location
Fig. 2

(a) Linearly graded velocity map, (b)–(d) application on manually created regions, and (e)–(h) application on regions based on real maps

Grahic Jump Location
Fig. 3

(a) Polar velocity map, (b)–(d) application on manually created regions, and (e)–(h) application on regions based on real maps

Grahic Jump Location
Fig. 4

(a) Range of exploration completion times for USVs over 200 simulations in the form of box plot and (b) variation of computational time with respect to number of USVs

Grahic Jump Location
Fig. 5

Variation of optimal times for area partitioning in the absence of correct velocity map, when we use either (a) constant map or (b) noisy map



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