Research Papers

Tracking Multiple Ground Targets in Urban Environments Using Cooperating Unmanned Aerial Vehicles

[+] Author and Article Information
Vitaly Shaferman

Institute of Automation and Control,
Vienna Institute of Technology,
Vienna 1040, Austria
e-mail: shaferman@acin.tuwien.ac.at

Tal Shima

Associate Professor
Department of Aerospace Engineering,
Technion - Israel Institute of Technology,
Haifa 32000, Israel
e-mail: tal.shima@technion.ac.il

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received June 7, 2014; final manuscript received September 6, 2014; published online December 10, 2014. Assoc. Editor: Jwu-Sheng Hu.

J. Dyn. Sys., Meas., Control 137(5), 051010 (May 01, 2015) (11 pages) Paper No: DS-14-1248; doi: 10.1115/1.4028594 History: Received June 07, 2014; Revised September 06, 2014; Online December 10, 2014

A distributed approach is proposed for planning a cooperative tracking task for a team of heterogeneous unmanned aerial vehicles (UAVs) tracking multiple predictable ground targets in a known urban environment. The solution methodology involves finding visibility regions, from which the UAV can maintain line-of-sight to each target during the scenario, and restricted regions, in which a UAV cannot fly, due to the presence of buildings or other airspace limitations. These regions are then used to pose a combined task assignment and motion planning optimization problem, in which each UAV's cost function is associated with its location relative to the visibility and restricted regions, and the tracking performance of the other UAVs in the team. A distributed co-evolution genetic algorithm (CEGA) is derived for solving the optimization problem. The proposed solution is scalable, robust, and computationally parsimonious. The algorithm is centralized, implementing a distributed computation approach; thus, global information is used and the computational workload is divided between the team members. This enables the execution of the algorithm in relatively large teams of UAVs servicing a large number of targets. The viability of the algorithm is demonstrated in a Monte Carlo study, using a high fidelity simulation test-bed incorporating a visual database of an actual city.

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Shima, T., Rasmussen, J. S., Sparks, G. A., and Passino, M. K., 2005, “Multiple Task Assignments for Cooperating Uninhabited Aerial Vehicles Using Genetic Algorithms,” Comput. Oper. Res., 33(11), pp. 3252–3269. [CrossRef]
Rasmussen, J. S., and Shima, T., 2008, “Tree Search for Assigning Cooperating UAVs to Multiple Tasks,” Int. J. Robust Nonlinear Control, 18(2), pp. 135–153. [CrossRef]
Shima, T., Rasmussen, S., and Gross, D., 2007, “Assigning Micro UAVs to Task Tours in an Urban Terrain,” IEEE Trans. Control Syst. Technol., 15(4), pp. 601–612. [CrossRef]
Shanmugavel, M., Tsourdos, A., White, B., and Żbikowski, R., 2010, “Co-Operative Path Planning of Multiple UAVs Using Dubins Paths With Clothoid Arcs,” Control Eng. Pract., 18(9), pp. 1084–1092. [CrossRef]
Shima, T., Rasmussen, S., and Chandler, P., 2007, “UAV Team Decision and Control Using Efficient Collaborative Estimation,” ASME J. Dyn. Syst. Meas. Contr., 129(5), pp. 609–619. [CrossRef]
Sinha, A., and Ghose, D., 2007, “Control of Multi-Agent Systems Using Linear Cyclic Pursuit With Heterogenous Controller Gains,” ASME J. Dyn. Syst. Meas. Contr., 129(5), pp. 742–748. [CrossRef]
Shaferman, V., and Shima, T., 2008, “Unmanned Aerial Vehicles Cooperative Tracking of Moving Ground Target in Urban Environments,” J. Guid. Control Dyn., 31(5), pp. 1360–1371. [CrossRef]
Dubins, L., 1957, “On Curves of Minimal Length With a Constraint on Average Curvature, and With Prescribed Initial and Terminal Position,” Am. J. Math., 79(3), pp. 497–516. [CrossRef]
Shanmugavel, M., Tsourdos, A., White, A. B., and Zbikowski, R., 2007, “Differential Geometric Path Planning of Multiple UAVs,” ASME J. Dyn. Syst. Meas. Contr., 129(5), pp. 620–632. [CrossRef]
Back, T., Fogel, B. D., and Michalewicz, Z., 2000, Evolutionary Computation Part 2, Advanced Algorithms and Operators, Institute of Physics in Press, Bristol, PA.
Sefrioui, M., and Periaux, J., 2000, “Nash Genetic Algorithms: Examples and Applications,” Proceedings of the IEEE Congress on Evolutionary Computation, La Jolla, CA, July 16–19, pp. 509–516. [CrossRef]
Shaferman, V., and Shima, T., 2008, “Co-Evolution Genetic Algorithms for UAV Distributed Tracking in Urban Environments,” ASME Paper No. ESDA2008-59590. [CrossRef]
Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA.
Spall, C. J., 2003, Introduction to Stochastic Search and Optimization, Wiley-Interscience, Hoboken, NJ. [CrossRef]
Xiao, J., Michalewicz, Z., Zhang, L., and Trojanowski, K., 1997, “Adaptive Evolutionary Planner/Navigator for Mobile Robots,” IEEE Trans. Evol. Comput., 1(1), pp. 18–28. [CrossRef]
Fogel, B. D., and Fogel, J. L., 1990, “Optimal Routing of Multiple Autonomous Under Water Vehicles Through Evolutionary Programming,” Proceedings of the 1990 Symposium on Autonomous Underwater Vehicle Technology, San Diego, CA, June 5–6, pp. 44–47. [CrossRef]
Capozzi, J. B., 2001, “Evolution-Based Path Planning and Management for Autonomous Vehicles,” Ph.D. thesis, University of Washington, Seattle, WA.
Michalewicz, Z., and Fogel, B. D., 2004, How to Solve It: Modern Heuristics, Springer, Berlin, Heidelberg, Germany. [CrossRef]
Nash, J. F., 1951, “Noncooperative Games,” Ann. Math., 54(2), pp. 54–289. [CrossRef]
Goodman, E. J., and O'Rourke, J., 1997, Handbook of Discrete and Computational Geometry, CRC Press, Boca Raton, FL.


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Fig. 1

Planar view of the tracking geometry

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Fig. 2

Control input encoding

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Fig. 3

Crossover operators

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Fig. 4

A map of the single target test scenario

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Fig. 5

A map of the multiple target test scenario

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Fig. 6

Sample run of two homogeneous UAVs tracking a single target

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Fig. 7

CETP, homogeneous UAV team, and single target

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Fig. 8

CETP sensitivity to the number of UAVs, homogeneous UAV team, and single target

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Fig. 9

CETP and GAMUTP comparisons, homogeneous UAV team, and single target

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Fig. 10

Sample run of two heterogeneous UAVs tracking two targets

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Fig. 11

CETP, heterogeneous UAV team and two targets

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Fig. 12

CETP sensitivity to the number of UAVs, heterogeneous UAV team, and two targets



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