Distributed Coordination of Large UAV teams with Limited and Intermittent Communication

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$69,890.00
Award Year:
2007
Program:
STTR
Phase:
Phase I
Contract:
N00014-07-M-0423
Award Id:
83437
Agency Tracking Number:
N074-019-0071
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
3 Innovation Way, Suite 100, Newark, DE, 19711
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
077990047
Principal Investigator:
Ganesh Vaidyanathan
Director
(302) 894-8044
gv@quantumleap.us
Business Contact:
Frank Abbott
VP of Administration & Finance, CFO
(302) 894-8045
fta@quantumleap.us
Research Institute:
UNIV. OF PENNSYLVANIA
Vijay Kumar
100 To Be Determined Road
Philadelphia, PA, 10000
(215) 898-3630
Nonprofit college or university
Abstract
Efficient coordination among heterogeneous Unmanned Aerial Vehicles (UAVs) promises to revolutionize the way in which complex tasks such as battlefield surveillance and support can be performed. However, current algorithms are not capable of achieving efficient and effective coordination such as dynamic task allocation and reallocation among large numbers of UAVs due to either the severe bottleneck of centralized algorithms or assumptions of perfect and unlimited communication. In this STTR, Quantum Leap Innovations (QLI), in conjunction with the University of Pennsylvania, proposes to construct a mathematically rigorous framework for evaluation and refinement of existing distributed coordination and control algorithms for large numbers of UAVs in the presence of limited, intermittent and asynchronous communication. Two key features of the proposed highly innovative framework include (1) the development of Token- Based Distributed Constraint Optimization (DCOP) algorithm for dynamic prioritysensitive task allocation and reallocation; (2) the introduction of limited and intermittent communication to the existing motion planning algorithm for the computation of trajectories for a cluster of UAVs. In Phase I, we will focus on development and integration of the high-level DCOP-based coordination algorithm for dynamic task allocation/reallocation and low-level robust motion planning algorithm with real-time spatial and temporal constraints. The feasibility of this approach will be demonstrated with a simple and user-friendly human interface that can enable multiple operators to manage large numbers of UAVs for their time-critical intelligence needs in a simplified simulation for battlefield search and coverage. In addition, Phase I option will involve the design of a framework in which we will provide a quantitative analysis of the success likelihood for a sequence of tasks from different operators and ensure each operator has adequate understanding of the capability of the UAV system and the process of tasks allocation and execution.

* information listed above is at the time of submission.

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