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TECHNICAL PAPERS

UAV Team Decision and Control Using Efficient Collaborative Estimation

[+] Author and Article Information
Tal Shima

Department of Aerospace Engineering,  Technion-Israel Institute of Technology, Haifa 32000, Israeltal.shima@technion.ac.il

Steven J. Rasmussen

Control Science Center of Excellence, Air Force Research Laboratory, Wright-Patterson AFB, OH 45433steven.rasmussen@wpafb.af.mil

Phillip Chandler

Control Science Center of Excellence, Air Force Research Laboratory, Wright-Patterson AFB, OH 45433phillip.chandler@wpafb.af.mil

http:∕∕www.isr.us∕researcẖsim̱muav.asp

J. Dyn. Sys., Meas., Control 129(5), 609-619 (Apr 21, 2007) (11 pages) doi:10.1115/1.2764504 History: Received June 05, 2006; Revised April 21, 2007

A novel decision-estimation methodology for a team of agents cooperating under communication imperfections is presented. The scenario of interest is that of a group of uninhabited aerial vehicles (UAVs) cooperatively performing, under communication delays, multiple tasks on multiple ground targets. In the proposed architecture, each UAV in the group runs an identical centralized decision algorithm and multiple information filters in parallel on its own states, its teammates’ states, and its own states as viewed by its teammates. Under perfect information, the decision architecture allows implicit coordination. Under imperfect information, the estimation of team members’ states enables predicting their cost to prosecute new tasks. Thus, the group performance under communication imperfections can be improved. Two different algorithms are proposed for the estimation process. The first is communication efficient, in which asynchronous information updates are sent to the network by individual members based on the value of the information to the rest of the group. The second is computation efficient utilizing synchronous information updates. Taking into account that the plan and plant of each UAV are known to the group improves the overall estimation process. Utilizing the MULTIUAV2 simulation testbed, a Monte Carlo study is presented. The benefit of using the proposed algorithms is shown with regard to the target prosecution rate and the communication bandwidth required for cooperation.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Example trajectories

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Estimation example of a computation efficient algorithm

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Estimation example of a communication efficient algorithm

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Figure 6

Communication sample run—no delay

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Communication sample run—computation efficient algorithm

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Figure 8

Communication sample run—communication efficient algorithm

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Figure 9

Distribution of maximum data rate—no delay

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Distribution of maximum data rate—computation efficient algorithm td=5s

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Figure 11

Distribution of maximum data rate—communication efficient algorithm td=5s

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Figure 12

Average number of targets prosecuted

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Figure 2

Target state transition diagram

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Team decision and control metrics

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