Trajectory optimization with risk minimization for military aircraft

Vian, John; Moore, John R.

doi:10.2514/3.20410

Cited by 59 publications

(22 citation statements)

References 2 publications

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“…The cost function contains components that penalize travelling time, fuel burn, and flying through penalty areas. 16 The cost function is defined as…”

Section: A Aircraft Model and Horizontal Trajectory Generationmentioning

confidence: 99%

Aircraft Trajectory Optimization and Contrails Avoidance in the Presence of Winds

Chen

2010

10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference

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There are indications that persistent contrails can lead to adverse climate change, although the complete effect on climate forcing is still uncertain. A flight trajectory optimization algorithm with fuel and contrails models, which develops alternative flight paths, provides policy makers the necessary data to make tradeoffs between persistent contrails mitigation and aircraft fuel consumption. This study develops an algorithm that calculates wind-optimal trajectories for cruising aircraft while avoiding the regions of airspace prone to persistent contrails formation. The optimal trajectories are developed by solving a non-linear optimal control problem with path constraints. The regions of airspace favorable to persistent contrails formation are modeled as penalty areas that aircraft should avoid and are adjustable. The tradeoff between persistent contrails formation and additional fuel consumption is investigated, with and without altitude optimization, for 12 city-pairs in the continental United States. Without altitude optimization, the reduction in contrail travel times is gradual with increase in total fuel consumption. When altitude is optimized, a two percent increase in total fuel consumption can reduce the total travel times through contrail regions by more than six times. Allowing further increase in fuel consumption does not seem to result in proportionate decrease in contrail travel times.

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“…The cost function contains components that penalize travelling time, fuel burn, and flying through penalty areas. 16 The cost function is defined as…”

Section: A Aircraft Model and Horizontal Trajectory Generationmentioning

confidence: 99%

Aircraft Trajectory Optimization and Contrails Avoidance in the Presence of Winds

Chen

2010

10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference

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“…These systems are required to take advantage of all available information in order to perform integrated task processing, reduce pilot workloads and provide updates at regular time intervals sufficient for threat avoidance [19]. A model for optimal routing an aircraft in a threat environment is developed based on specified mission objectives, available resources (fuel capacity), limitations on aircraft control while minimizing risk exposure.…”

Section: ) Variable Aircraft Radar Cross-section (Rcs); 2) Differentmentioning

confidence: 99%

“…Efficiency of discrete optimization approaches in optimal risk path planning essentially depends on type of risk functionals, technological constraints, and a scheme of trajectory approximation (see, for instance, [19] for discussions of these issues). Gradient-based algorithms are very efficient when the risk of detection is determined by smooth analytical functionals.…”

Section: ■ Gradient-based Algorithms M Dynamic Programming ■ Network mentioning

confidence: 99%

“…These methods tend to be computationally intense and, therefore, are not well suited for onboard applications. To improve computation time, John and Moore [19] used simple analytical risk functions. Based on such an approach, they developed lateral and vertical algorithms to optimize flight trajectory with respect to time, fuel, aircraft final position, and risk exposure.…”

Section: ■ Gradient-based Algorithms M Dynamic Programming ■ Network mentioning

confidence: 99%

“…It addresses several types of problems with various objectives, constraints on resources and control limitations, for instance, ■ Minimizing risk of aircraft detection by radars, sensors or surface air missiles (SAM) [5,19,22] ■ Minimizing risk of submarine detection by sensors [21] ■ Minimizing cumulative radiation damage in passing through a contaminated area ■ Finding optimal trajectories for multiple aircraft avoiding collisions [15] ■ Maximizing the probability of target detecting by a searcher [1,3,9,12,13,16,17,20] …”

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Robust Decision Making: Addressing Uncertainties in Distributions

Uryasev¹,

Pardalos²

2004

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including ■ gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Servic 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be awars that notwiths penalty for failing to comply with a collection of information if it does not display a currently valid 0MB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.AFRL-SR-AR-TR-04- PERFORMING ORGANIZATION NAME{S) AND ADDRESS(ES)University of Florida 310 Weil Hall Gainesville, FL 32611-6550 SPONSORING/MONITORING AGENCY NAME{S) AND ADDRESS(ES)Air SummaryThe project was concentrated on development of new methodologies for decision making in uncertain environment and relevant applications. The first part of the project was focused on analytical and discrete optimization approaches for routing an aircraft in threat environment. The model considered aircraft trajectory in three-dimensional space. Several threats were studied, including risk of aircraft detection by radars, sensors, and the risk of being killed by surface to air missiles. The problem of finding aircraft optirhal risk trajectory subject to a constraint on the trajectory length was solved by analytical and discrete optimization approaches.The second part of the project resulted in general approach to risk management for the case with uncertainties in distributions. The risk of loss, damage, or failure was measured by the Conditional Value-at-Risk (CVaR) measure. As a function of decision variables, CVaR is convex, and therefore can be efficiendy controlled/optimized using convex or linear programming. The methodology was tested on two Weapon-Target Assignment (WTA) problems. The total cost of a mission was minimized, while satisfying the operational constraints and ensuring destruction of targets with high probability. The risk of a failure of the mission is controlled by CVaR constraints. The case studies showed that there were significant qualitative and quantitative differences in solutions of deterministic and stochastic WTA problems.The third part of the project studied the Multiple Traveling Salesmen Problem (Multiple-TSP) in several variations. The research was focused on MIN-MAX 2-TSP, which cannot be solved by standard methods. The relation between this class of problems and a subclass of the self-dual monotonic Boolean functions was established. This resulted in new efficient optimization algorithms.

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Obstacle Avoidance: Static Obstacles

Shanmugavel

Tsourdos

White

2010

Encyclopedia of Aerospace Engineering

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This article presents an overview of obstacle avoidance of static obstacles of autonomous vehicles. Two methods are discussed to handle the known and unknown obstacles. A brief introduction of road map methods are discussed which are used to avoid static obstacles which are known a priori. The pop‐up or unknown obstacles are handled by reactive method such as potential field and by replanning the paths. These methods avoid the obstacles by changing the original path produced by path planning. Relevant research on the obstacle avoidance is presented where optimization and other approaches are discussed.

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Trajectory optimization with risk minimization for military aircraft

Cited by 59 publications

References 2 publications

Aircraft Trajectory Optimization and Contrails Avoidance in the Presence of Winds

Aircraft Trajectory Optimization and Contrails Avoidance in the Presence of Winds

Robust Decision Making: Addressing Uncertainties in Distributions

Obstacle Avoidance: Static Obstacles

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