0
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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

Shah, B. C. , Švec, P. , Bertaska, I. R. , Klinger, W. , Sinisterra, A. J. , von Ellenrieder, K. , Dhanak, M. , and Gupta, S. K. , 2016, “ Resolution-Adaptive Risk-Aware Trajectory Planning for Surface Vehicles Operating in Congested Civilian Traffic,” Auton. Robots, 40(7), pp. 1139–1163. [CrossRef]
Shah, B. C. , and Gupta, S. K. , 2016, “ Speeding Up A* Search on Visibility Graphs Defined Over Quadtrees to Enable Long Distance Path Planning for Unmanned Surface Vehicles,” International Conference on Automated Planning and Scheduling (ICAPS), London, June 12–17, pp. 527–535. https://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13155/12717
Bertaska, I. R. , Shah, B. C. , von Ellenrieder, K. , Švec, P. , Klinger, W. , Sinisterra, A. J. , Dhanak, M. , and Gupta, S. K. , 2015, “ Experimental Evaluation of Automatically-Generated Behaviors for USV Operations,” Ocean Eng., 106, pp. 496–514. [CrossRef]
Švec, P. , Thakur, A. , Raboin, E. , Shah, B. C. , and Gupta, S. K. , 2014, “ Target Following With Motion Prediction for Unmanned Surface Vehicle Operating in Cluttered Environments,” Auton. Robots, 36(4), pp. 383–405. [CrossRef]
Raboin, E. , Švec, P. , Nau, D. S. , and Gupta, S. K. , 2015, “ Model-Predictive Asset Guarding by Team of Autonomous Surface Vehicles in Environment With Civilian Boats,” Auton. Robots, 38(3), pp. 261–282. [CrossRef]
Sheng, W. , Yang, Q. , Tan, J. , and Xi, N. , 2006, “ Distributed Multi-Robot Coordination in Area Exploration,” Rob. Auton. Syst., 54(12), pp. 945–955. [CrossRef]
Burgard, W. , Moors, M. , Stachniss, C. , and Schneider, F. E. , 2005, “ Coordinated Multi-Robot Exploration,” IEEE Trans. Rob., 21(3), pp. 376–386. [CrossRef]
Grocholsky, B. , Keller, J. , Kumar, V. , and Pappas, G. , 2006, “ Cooperative Air and Ground Surveillance,” IEEE Rob. Autom. Mag., 13(3), pp. 16–25. [CrossRef]
Roy, N. , and Dudek, G. , 2001, “ Collaborative Robot Exploration and Rendezvous: Algorithms, Performance Bounds and Observations,” Auton. Robots, 11(2), pp. 117–136. [CrossRef]
Shriyam, S. , Shah, B. C. , and Gupta, S. K. , 2017, “On-Line Task Decomposition for Collaborative Surveillance of Marine Environment by a Team of Unmanned Surface Vehicles,” ASME Paper No. DETC2017-67972.
Pavone, M. , Arsie, A. , Frazzoli, E. , and Bullo, F. , 2011, “ Distributed Algorithms for Environment Partitioning in Mobile Robotic Networks,” IEEE Trans. Autom. Control, 56(8), pp. 1834–1848. [CrossRef]
Lien, J.-M. , and Amato, N. M. , 2006, “ Approximate Convex Decomposition of Polygons,” Comput. Geom., 35(1–2), pp. 100–123. [CrossRef]
Jager, M. , and Nebel, B. , 2002, “ Dynamic Decentralized Area Partitioning for Cooperating Cleaning Robots,” IEEE International Conference on Robotics and Automation (ICRA), Washington, DC, May 11–15, pp. 3577–3582.
Ahmadi, M. , and Stone, P. , 2006, “ A Multi-Robot System for Continuous Area Sweeping Tasks,” IEEE International Conference on Robotics and Automation (ICRA), Orlando, FL, May 15–19, pp. 1724–1729.
Choset, H. , 2001, “ Coverage for Robotics–A Survey of Recent Results,” Ann. Math. Artif. Intell., 31(1), pp. 113–126. [CrossRef]
Zelinsky, A. , Jarvis, R. A. , Byrne, J. , and Yuta, S. , 1993, “ Planning Paths of Complete Coverage of an Unstructured Environment by a Mobile Robot,” International Conference on Advanced Robotics (ICAR), Tokyo, Japan, Nov. 1–2, pp. 533–538. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=7A4019C71C14501B4BCBC1C1FAA496A3?doi=10.1.1.53.7617&rep=rep1&type=pdf
Galceran, E. , and Carreras, M. , 2013, “ A Survey on Coverage Path Planning for Robotics,” Rob. Auton. Syst., 61(12), pp. 1258–1276. [CrossRef]
Zheng, X. , and Koenig, S. , 2007, “ Robot Coverage of Terrain With Non-Uniform Traversability,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Diego, CA, Oct. 29–Nov. 2, pp. 3757–3764.
Kennedy, J. , and Eberhart, R. , 1995, “ Particle Swarm Optimization,” IEEE International Conference on Neural Networks, Perth, Australia, Nov. 27–Dec. 1, pp. 1942–1948.

Figures

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

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In