24,490 research outputs found
Integrated risk/cost planning models for the US Air Traffic system
A prototype network planning model for the U.S. Air Traffic control system is described. The model encompasses the dual objectives of managing collision risks and transportation costs where traffic flows can be related to these objectives. The underlying structure is a network graph with nonseparable convex costs; the model is solved efficiently by capitalizing on its intrinsic characteristics. Two specialized algorithms for solving the resulting problems are described: (1) truncated Newton, and (2) simplicial decomposition. The feasibility of the approach is demonstrated using data collected from a control center in the Midwest. Computational results with different computer systems are presented, including a vector supercomputer (CRAY-XMP). The risk/cost model has two primary uses: (1) as a strategic planning tool using aggregate flight information, and (2) as an integrated operational system for forecasting congestion and monitoring (controlling) flow throughout the U.S. In the latter case, access to a supercomputer is required due to the model's enormous size
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
Inverse Optimal Planning for Air Traffic Control
We envision a system that concisely describes the rules of air traffic
control, assists human operators and supports dense autonomous air traffic
around commercial airports. We develop a method to learn the rules of air
traffic control from real data as a cost function via maximum entropy inverse
reinforcement learning. This cost function is used as a penalty for a
search-based motion planning method that discretizes both the control and the
state space. We illustrate the methodology by showing that our approach can
learn to imitate the airport arrival routes and separation rules of dense
commercial air traffic. The resulting trajectories are shown to be safe,
feasible, and efficient
- …