Algorithms for air traffic flow management under stochastic environments

Nilim, Arnab; Ghaoui, Laurent El

doi:10.23919/acc.2004.1384440

Cited by 29 publications

(15 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is done through the use of jet streams and real-time weather forecasts of high-altitude wind currents [15]. The stochastic and dynamic behavior of weather obstacles, such as storms is also studied [16]. In the world of UAVs, evolutionary algorithms are used to find an adaptive optimized path, using large-scale wind fields, e.g., for a Trans-Pacific Crossing [8], or adaptive obstacle avoidance [6].…”

Section: Related Workmentioning

confidence: 99%

A Study of the Concept and Implementation of the Lead Systems Integrator in Defense Acquisitions

Harriott¹,

Santamaria²

2007

View full text Add to dashboard Cite

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to ABSTRACTFuel or battery consumption of unmanned aerial vehicles (UAVs) can be improved by utilizing or avoiding air currents. This thesis adopts a network modeling approach to formulate the problem of finding minimum energy flight paths. The relevant airspace is divided into small regions using a grid of nodes, inter-connected by arcs. A function, representing energy cost, is defined on every arc in terms of the solution of a constrained nonlinear program for the optimal local airspeed to fly in a given wind field. Then, shortest-path models are implemented on the network to find the optimal paths from an origin to a destination. Five models are studied and they correspond to cases of pre-planning of flight routes and dynamic updating of routes during the course of the flight. These models use three-dimensional grids of forecasted wind currents, produced by the Naval Research Laboratory's Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) with horizontal resolution of 1 km. One of the shortest-path models, a stochastic-dynamic model, assumes real-time measurement capabilities of the wind velocity in the vicinity of the UAV, through its GPS-INS system, and provides updated waypoints to follow after every measurement. For each model, the energy costs of the shortest-path solutions for 1000 randomized missions over a Nevada test site are simulated and compared to the energy costs of straight-line paths. For a 100 kg UAV, the dynamic model produces an average reduction of 15.1% in the energy consumption along 40 km long round trips, and an average reduction of 30.1% under windy conditions with average wind speeds larger than 15 m/s. A stochastic-dynamic model for maximum duration, solved using a heuristic algorithm, achieves an average increase of 32.2% in the flight duration for a 100 kg UAV. NUMBER OF PAGES 8514. SUBJECT TERMS Optimization, Unmanned Aerial Vehicles, Stochastic-Dynamic Shortest Path, Weather Forecasts, Aerodynamics, Correlation Structure. PRICE CODE SECURITY CLASSIFICATION OF REPORT Unclassified SECURITY CLASSIFICATION OF THIS PAGE Unclassified SECURITY CLASSIFICATION OF ABSTRACT Unclassified LIMITATION OF ABSTRACT MINIMUM-ENERGY FLIGHT PATHS FOR UAVS USING MESOSCALE WIND FORECASTS AND APPROXIMATE DYNAMIC PROGRAMMINGGil Nachmani Captain, Israel Defense Forces B.Sc., Hebrew University of Jerusalem, 2001 Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN OPERATIONS RESEARCHfrom the NAVAL POSTGRADUATE SCHOOL December 2007Author: Gil Nachmani

show abstract

Section: Related Workmentioning

confidence: 99%

A Study of the Concept and Implementation of the Lead Systems Integrator in Defense Acquisitions

Harriott¹,

Santamaria²

2007

View full text Add to dashboard Cite

show abstract

“…For example, Nilim and his colleagues model the weather as Markov chains where storms have a certain probability of becoming the obstacles, thus preventing the airplanes from passing through those regions. Then, a path finding model identifies a path minimizing the expected travel time and dynamically reevaluates the path as more accurate information about the storms becomes available [50,49]. In their work, Nilim et al assume that the airplanes have a constant velocity, consequently reducing the problem to a shortest path finding problem among stochastic obstacles.…”

Section: Our Colleagues Working On the Optimum Vessel Performance In mentioning

confidence: 99%

Optimal path finding in direction, location, and time dependent environments

Dolinskaya

2012

Naval Research Logistics

View full text Add to dashboard Cite

This article examines optimal path finding problems where cost function and constraints are direction, location, and time dependent. Recent advancements in sensor and data‐processing technology facilitate the collection of detailed real‐time information about the environment surrounding a ground vehicle, an airplane, or a naval vessel. We present a navigation model that makes use of such information. We relax a number of assumptions from existing literature on path‐finding problems and create an accurate, yet tractable, model suitable for implementation for a large class of problems. We present a dynamic programming model which integrates our earlier results for direction‐dependent, time and space homogeneous environment, and consequently, improves its accuracy, efficiency, and run‐time. The proposed path finding model also addresses limited information about the surrounding environment, control‐feasibility of the considered paths, such as sharpest feasible turns a vehicle can make, and computational demands of a time‐dependent environment. To demonstrate the applicability and performance of our path‐finding algorithm, computational experiments for a short‐range ship routing in dynamic wave‐field problem are presented. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012

show abstract

“…In RDO, some of the main parameters are regarded as stochastic variables, possibly with a known probability distribution coming from previous experiences or theoretical considerations. The concept of RDO has been firstly introduced by Taguchi in late fifties, and than further developed in [1,2]: this represents a really active research area, with practical applications in the majority of the industrial fields [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Sequential quadrature methods for RDO

Peri

2016

Communications in Applied and Industrial Mathematics

View full text Add to dashboard Cite

This paper presents a comparative study between a large number of different existing sequential quadrature schemes suitable for Robust Design Optimization (RDO), with the inclusion of two partly original approaches. Efficiency of the different integration strategies is evaluated in terms of accuracy and computational effort: main goal of this paper is the identification of an integration strategy able to provide the integral value with a prescribed accuracy using a limited number of function samples. Identification of the different qualities of the various integration schemes is obtained utilizing both algebraic and practical test cases. Differences in the computational effort needed by the different schemes is evidenced, and the implications on their application to practical RDO problems is highlighted.

show abstract

Algorithms for air traffic flow management under stochastic environments

Cited by 29 publications

References 2 publications

A Study of the Concept and Implementation of the Lead Systems Integrator in Defense Acquisitions

A Study of the Concept and Implementation of the Lead Systems Integrator in Defense Acquisitions

Optimal path finding in direction, location, and time dependent environments

Sequential quadrature methods for RDO

Contact Info

Product

Resources

About