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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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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