1,829 research outputs found
A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption
The past decade has seen a substantial increase in the use of small unmanned
aerial vehicles (UAVs) in both civil and military applications. This article
addresses an important aspect of refueling in the context of routing multiple
small UAVs to complete a surveillance or data collection mission. Specifically,
this article formulates a multiple-UAV routing problem with the refueling
constraint of minimizing the overall fuel consumption for all of the vehicles
as a two-stage stochastic optimization problem with uncertainty associated with
the fuel consumption of each vehicle. The two-stage model allows for the
application of sample average approximation (SAA). Although the SAA solution
asymptotically converges to the optimal solution for the two-stage model, the
SAA run time can be prohibitive for medium- and large-scale test instances.
Hence, we develop a tabu-search-based heuristic that exploits the model
structure while considering the uncertainty in fuel consumption. Extensive
computational experiments corroborate the benefits of the two-stage model
compared to a deterministic model and the effectiveness of the heuristic for
obtaining high-quality solutions.Comment: 18 page
Path Planning for Cooperative Routing of Air-Ground Vehicles
We consider a cooperative vehicle routing problem for surveillance and
reconnaissance missions with communication constraints between the vehicles. We
propose a framework which involves a ground vehicle and an aerial vehicle; the
vehicles travel cooperatively satisfying the communication limits, and visit a
set of targets. We present a mixed integer linear programming (MILP)
formulation and develop a branch-and-cut algorithm to solve the path planning
problem for the ground and air vehicles. The effectiveness of the proposed
approach is corroborated through extensive computational experiments on several
randomly generated instances
A Scalable Low-Cost-UAV Traffic Network (uNet)
This article proposes a new Unmanned Aerial Vehicle (UAV) operation paradigm
to enable a large number of relatively low-cost UAVs to fly
beyond-line-of-sight without costly sensing and communication systems or
substantial human intervention in individual UAV control. Under current
free-flight-like paradigm, wherein a UAV can travel along any route as long as
it avoids restricted airspace and altitudes. However, this requires expensive
on-board sensing and communication as well as substantial human effort in order
to ensure avoidance of obstacles and collisions. The increased cost serves as
an impediment to the emergence and development of broader UAV applications. The
main contribution of this work is to propose the use of pre-established route
network for UAV traffic management, which allows: (i) pre- mapping of obstacles
along the route network to reduce the onboard sensing requirements and the
associated costs for avoiding such obstacles; and (ii) use of well-developed
routing algorithms to select UAV schedules that avoid conflicts. Available
GPS-based navigation can be used to fly the UAV along the selected route and
time schedule with relatively low added cost, which therefore, reduces the
barrier to entry into new UAV-applications market. Finally, this article
proposes a new decoupling scheme for conflict-free transitions between edges of
the route network at each node of the route network to reduce potential
conflicts between UAVs and ensuing delays. A simulation example is used to
illustrate the proposed uNet approach.Comment: To be submitted to journal, 21 pages, 9 figure
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