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UAV Routing and Coordination in Stochastic, Dynamic Environments

  • John J. Enright
  • Emilio Frazzoli
  • Marco Pavone
  • Ketan Savla
Reference work entry

Abstract

Recent years have witnessed great advancements in the science and technology for unmanned aerial vehicles (UAVs), for example, in terms of autonomy, sensing, and networking capabilities. This chapter surveys algorithms on task assignment and scheduling for one or multiple UAVs in a dynamic environment, in which targets arrive at random locations at random times, and remain active until one of the UAVs flies to the target’s location and performs an on-site task. The objective is to minimize some measure of the targets’ activity, for example, the average amount of time during which a target remains active. The chapter focuses on a technical approach that relies upon methods from queueing theory, combinatorial optimization, and stochastic geometry. The main advantage of this approach is its ability to provide analytical estimates of the performance of the UAV system on a given problem, thus providing insight into how performance is affected by design and environmental parameters, such as the number of UAVs and the target distribution. In addition, the approach provides provable guarantees on the system’s performance with respect to an ideal optimum. To illustrate this approach, a variety of scenarios are considered, ranging from the simplest case where one UAV moves along continuous paths and has unlimited sensing capabilities, to the case where the motion of the UAV is subject to curvature constraints, and finally to the case where the UAV has a finite sensor footprint. Finally, the problem of cooperative routing algorithms for multiple UAVs is considered, within the same queueing-theoretical framework, and with a focus on control decentralization.

Keywords

Travel Salesman Problem Online Algorithm Voronoi Region Motion Constraint Differential Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Kiva SystemsNorth ReadingUSA
  2. 2.Department of Aeronautics and AstronauticsMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.Aeronautics and Astronautics DepartmentStanford UniversityStanfordUSA
  4. 4.Sonny Astani Department of Civil and Environmental EngineeringUniversity of Southern CaliforniaLos AngelesUSA

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