Multiple Method 2-D Trajectory Optimization Satisfying Waypoints and No-Fly Zone Constraints

Jorris, Timothy R.; Cobb, Richard

doi:10.2514/1.32354

Cited by 87 publications

(47 citation statements)

References 9 publications

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“…Flight in a horizontal plane 2-D was analyzed [5,6,17,33,44], along with considering the effects of constant winds [2][3][4]. Employing obstacle avoidance [2,[27][28][29][30][31][32] and target tracking [33][34][35][36][37][38][39][40][41] within the trajectory generation was addressed. Path planning for multiple UAV formation and collision free cooperative control and tactical applications have been considered [42,43].…”

Section: Introductionmentioning

confidence: 99%

An adaptive dual-optimal path-planning technique for unmanned air vehicles

Whitfield

2016

Int. J. Simul. Multisci. Des. Optim.

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-A multi-objective technique for unmanned air vehicle path-planning generation through task allocation has been developed. The dual-optimal path-planning technique generates real-time adaptive flight paths based on available flight windows and environmental influenced objectives. The environmentally-influenced flight condition determines the aircraft optimal orientation within a downstream virtual window of possible vehicle destinations that is based on the vehicle's kinematics. The intermittent results are then pursued by a dynamic optimization technique to determine the flight path. This path-planning technique is a multi-objective optimization procedure consisting of two goals that do not require additional information to combine the conflicting objectives into a single-objective. The technique was applied to solar-regenerative high altitude long endurance flight which can benefit significantly from an adaptive real-time path-planning technique. The objectives were to determine the minimum power required flight paths while maintaining maximum solar power for continual surveillance over an area of interest (AOI). The simulated path generation technique prolonged the flight duration over a sustained turn loiter flight path by approximately 2 months for a year of flight. The potential for prolonged solar powered flight was consistent for all latitude locations, including 2 months of available flight at 60°latitude, where sustained turn flight was no longer capable.

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Section: Introductionmentioning

confidence: 99%

An adaptive dual-optimal path-planning technique for unmanned air vehicles

Whitfield

2016

Int. J. Simul. Multisci. Des. Optim.

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“…Some of the most simple representations used in the literature include a two-dimensional (2D) position in the vertical plane [23,24] or in the horizontal plane [25][26][27][28]. The former are useful in the context of optimization in the vertical profile, while the latter can be used for optimizing traffic.…”

Section: Specification Of Trajectoriesmentioning

confidence: 99%

“…For example, in [26,27], the flight is described as a sequence of 2D waypoints. These waypoints can also include other information such as the altitude or course [33].…”

Section: Other Specifications Of Flightmentioning

confidence: 99%

“…These constraints are usually expressed through a domain of application (DoA) -i.e., the region of space where the constraint must be met-, and a restriction in the aircraft state -i.e., the condition that must be met, usually related to speed or altitude. DoAs may be restricted to simple geometrical shapes [26] or arbitrary shapes [37]. Sometimes, DoAs are defined by elements of the airspace structure, such as legs, routes, significant points, or 3D volumes.…”

Section: Additional Flight Informationmentioning

confidence: 99%

“…This information that can be used in trajectory optimization processes to minimize (or maximize) a certain cost (or merit) function evaluated over all or part of the trajectory. Usually, objectives are related to fuel consumption [29,31,34], flight duration [26,36,54], or a trade-off between these two factors [61].…”

Section: Additional Flight Informationmentioning

confidence: 99%

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Applications of Formal Languages to Management of Manned and Unmanned Aircraft

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Method for solving chance constrained optimal control problems using biased kernel density estimators

Keil

Miller

Kumar³

et al. 2020

Optim Control Appl Methods

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SummaryA method is developed to numerically solve chance constrained optimal control problems. The chance constraints are reformulated as nonlinear constraints that retain the probability properties of the original constraint. The reformulation transforms the chance constrained optimal control problem into a deterministic optimal control problem that can be solved numerically. The new method developed in this paper approximates the chance constraints using Markov Chain Monte Carlo sampling and kernel density estimators whose kernels have integral functions that bound the indicator function. The nonlinear constraints resulting from the application of kernel density estimators are designed with bounds that do not violate the bounds of the original chance constraint. The method is tested on a nontrivial chance constrained modification of a soft lunar landing optimal control problem and the results are compared with results obtained using a conservative deterministic formulation of the optimal control problem. Additionally, the method is tested on a complex chance constrained unmanned aerial vehicle problem. The results show that this new method can be used to reliably solve chance constrained optimal control problems.

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Multiple Method 2-D Trajectory Optimization Satisfying Waypoints and No-Fly Zone Constraints

Cited by 87 publications

References 9 publications

An adaptive dual-optimal path-planning technique for unmanned air vehicles

An adaptive dual-optimal path-planning technique for unmanned air vehicles

Applications of Formal Languages to Management of Manned and Unmanned Aircraft

Method for solving chance constrained optimal control problems using biased kernel density estimators

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