Hybridization of Nonlinear and Mixed-Integer Linear Programming for Aircraft Separation With Trajectory Recovery

The approach presented in this paper aims at finding a solution to the problem of conflict-free motion planning for multiple aircraft on the same flight level with trajectory recovery. One contribution of this work is to develop three consistent models, i.e., from a continuous-time representation to...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 14; no. 3; pp. 1218 - 1230
Main Authors: Omer, Jeremy, Farges, Jean-Loup
Format: Journal Article
Language:English
Published: IEEE 01.09.2013
Subjects:
Acceleration
Air traffic control (ATC)
Aircraft
Atmospheric modeling
Computational modeling
Computer Science
conflict resolution
Mathematics
mixed-integer linear programming (MILP)
nonlinear programming (NLP)
Operations Research
optimal control
Optimization
Optimization and Control
Planning
Trajectory
conflict resolution
optimal control
mixed integer linear programming
nonlinear programming
Air traffic control
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:The approach presented in this paper aims at finding a solution to the problem of conflict-free motion planning for multiple aircraft on the same flight level with trajectory recovery. One contribution of this work is to develop three consistent models, i.e., from a continuous-time representation to a discrete-time linear approximation. Each of these models guarantees separation at all times and trajectory recovery, but they are not equally difficult to solve. A new hybrid algorithm is thus developed to use the optimal solution of a mixed-integer linear program as a starting point when solving a nonlinear formulation of the problem. The significance of this process is that it always finds a solution when the linear model is feasible while still taking into account the nonlinear nature of the problem. A test bed containing numerous data sets is then generated from three virtual scenarios. A comparative analysis with three different initializations of nonlinear optimization validates the efficiency of the hybrid method.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2013.2257758