Generating Diverse Reroutes for Tactical Constraint Avoidance

Taylor, Christine; Liu, Sheng; Wan, Yan; Stewart, Timothy

doi:10.2514/1.d0089

Cited by 12 publications

(5 citation statements)

References 18 publications

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“…Opposite to the classical optimization approaches presented above, Path-Planning As an example, in [23], DSP algorithm is used in combination with a Voronoi diagram to optimize UAV flights in the presence of small and static obstacles. Moreover, the works from [9,11] designed DSP variations for rerouting around deterministic Weather Avoidance Fields. In [24], A * is used for storm avoidance in the vicinity of an airport.…”

Section: Trajectory Planning Methodologiesmentioning

confidence: 99%

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RRT*-Based Algorithm for Trajectory Planning Considering Probabilistic Weather Forecasts

Andrés

Kamgarpour

Soler

et al. 2021

Lecture Notes in Electrical Engineering

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Section: Trajectory Planning Methodologiesmentioning

confidence: 99%

“…In [10], the use of machine learning is suggested to automatically build a TOS based on historical data. Moreover, the authors in [11] address the problem with a multi-objective genetic algorithm that, based on different metrics (e.g., incursions in adverse weather regions), evaluates the acceptability of each member in a TOS.…”

Section: Operational Constraintsmentioning

confidence: 99%

RRT*-Based Algorithm for Trajectory Planning Considering Probabilistic Weather Forecasts

Andrés

Kamgarpour

Soler

et al. 2021

Lecture Notes in Electrical Engineering

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“…Regarding constraints, Erzberger et al (2016) describe algorithms that generate trajectories avoiding convection areas and compute scheduled arrival times, but they assume that convection areas are detected a priori by radar sensors. Taylor et al (2018) apply a multi-objective genetic algorithm that treats constrained flight planning as a multiobjective problem and computes a Pareto set of solutions. However, they do not reason about weather uncertainty, and the genetic algorithm does not provide any guarantees.…”

Section: Related Workmentioning

confidence: 99%

Optimal and Heuristic Approaches for Constrained Flight Planning under Weather Uncertainty

Geißer

Povéda

Trevizan

et al. 2020

ICAPS

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Aircraft flight planning is impacted by weather uncertainties. Existing approaches to flight planning are either deterministic and load additional fuel to account for uncertainty, or probabilistic but have to plan in 4D space. If constraints are imposed on the flight plan these methods provide no formal guarantees that the constraints are actually satisfied. We investigate constrained flight planning under weather uncertainty on discrete airways graphs and model this problem as a Constrained Stochastic Shortest Path (C-SSP) problem. Transitions are generated on-the-fly by the underlying aircraft performance model. As this prevents us from using off-the-shelf C-SSP solvers, we generalise column-generation methods stemming from constrained deterministic path planning to the probabilistic case. This results in a novel method which is complete but computationally expensive. We therefore also discuss deterministic and heuristic approaches which average over weather uncertainty and handle constraints by scalarising a multi-objective cost function. We evaluate and compare these approaches on real flight routes subject to real weather forecast data and a realistic aircraft performance model.

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“…An A * algorithm is employed in [196] to generate weather avoidance routes in a TRACON impacted by convective activity. In [197], the authors combine a variant of Dijkstra's algorithm with a multi-objective genetic algorithm in order to produce a set of proposed reroutes around convective weather, as represented by the FAA's Convective Avoidance Weather Model (CWAM). In [198], CWAM weather forecasts were employed in combination with Dijkstra's algorithm to minimize a combination of fuel burn and expected cost of deviation due to weather.…”

Section: Convective Environmentsmentioning

confidence: 99%

Robust aircraft trajectory planning under uncertain convective environments with optimal control and rapidly developing thunderstorms

González-Arribas

Soler

Sanjurjo-Rivo

et al. 2019

Aerospace Science and Technology

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The Air Traffic Management (ATM) system in the busiest airspaces in the world is currently being overhauled to deal with multiple capacity, socioeconomic, and environmental challenges. One major pillar of this process is the shift towards a concept of operations centered on aircraft trajectories (called Trajectory-Based Operations or TBO in Europe) instead of rigid airspace structures. However, its successful implementation (and, thus, the realization of the associated improvements in ATM performance) rests on appropriate understanding and management of uncertainty. Due to its complex socio-technical structure, the design and operations of the ATM system are heavily impacted by uncertainty, proceeding from multiple sources and propagating through the interconnections between its subsystems.One major source of ATM uncertainty is weather. Due to its nonlinear and chaotic nature, a number of meteorological phenomena of interest cannot be forecasted with complete accuracy at arbitrary lead times, which leads to uncertainty or disruption in individual air and ground operations that propagates to all ATM processes. Therefore, in order to achieve the goals of SESAR and similar programs, it is necessary to deal with meteorological uncertainty at multiple scales, from the trajectory prediction and planning processes to flow and traffic management operations. This thesis addresses the problem of single-aircraft flight planning considering two important sources of meteorological uncertainty: wind prediction error and convective activity. As the actual wind field deviates from its forecast, the actual trajectory will diverge in time from the planned trajectory, generating uncertainty in arrival times, sector entry and exit times, and fuel burn. Convective activity also impacts trajectory predictability, as it leads pilots to deviate from their planned route, creating challenging situations for controllers. In this work, we aim to develop algorithms and methods for aircraft trajectory optimization that are able to integrate information about the uncertainty in these meteorological phenomena into the flight planning process at both pre-tactical (before departure) and tactical horizons (while the aircraft is airborne), in order to generate more efficient and predictable trajectories.To that end, we frame flight planning as an optimal control problem, modeling the motion of the aircraft with a point-mass model and the BADA performance model. Optimal control methods represent a flexible and general approach that has a long history of success in the aerospace field. As a numerical scheme, we use direct methods, which can deal with nonlinear systems of moderate and high-dimensional state spaces in a computationally manageable way. Nevertheless, while this framework is well-developed in the context of deterministic problems, the techniques for the solution of practical ix x Abstract optimal control problems under uncertainty are not as mature, and the methods proposed in the literature are not applicable to the flight planning probl...

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Generating Diverse Reroutes for Tactical Constraint Avoidance

Cited by 12 publications

References 18 publications

RRT*-Based Algorithm for Trajectory Planning Considering Probabilistic Weather Forecasts

RRT*-Based Algorithm for Trajectory Planning Considering Probabilistic Weather Forecasts

Optimal and Heuristic Approaches for Constrained Flight Planning under Weather Uncertainty

Robust aircraft trajectory planning under uncertain convective environments with optimal control and rapidly developing thunderstorms

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