Computationally Efficient Trajectory Generation for Smooth Aircraft Flight Level Changes

Hong, Haichao; Piprek, Patrick; Gerdts, Matthias; Holzapfel, Florian

doi:10.2514/1.g005529

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…The idea is to reformulate a class of nonlinear guidance with path and terminal constraints into a generic quadratic programming (QP) and then to solve problems of the SQP concept. In this regard, a scheme with five foundational elements is developed and tailored to the requirements of missions by Reference [36].…”

Section: Trajectory Optimization Methodsmentioning

confidence: 99%

Flight Planning Optimization of Multiple UAVs for Internet of Things

Rodrigues

Riker

Ribeiro

et al. 2021

Sensors

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This article presents an approach to autonomous flight planning of Unmanned Aerial Vehicles (UAVs)-Drones as data collectors to the Internet of Things (IoT). We have proposed a model for only one aircraft, as well as for multiple ones. A clustering technique that extends the scope of the number of IoT devices (e.g., sensors) visited by UAVs is also addressed. The flight plan generated from the model focuses on preventing breakdowns due to a lack of battery charge to maximize the number of nodes visited. In addition to the drone autonomous flight planning, a data storage limitation aspect is also considered. We have presented the energy consumption of drones based on the aerodynamic characteristics of the type of aircraft. Simulations show the algorithm’s behavior in generating routes, and the model is evaluated using a reliability metric.

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Section: Trajectory Optimization Methodsmentioning

confidence: 99%

Flight Planning Optimization of Multiple UAVs for Internet of Things

Rodrigues

Riker

Ribeiro

et al. 2021

Sensors

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“…In our approach the time, 𝑡 𝑓 , at the end of the trajectory is unknown. A commonly-used method to deal with free final-time problems is to rewrite the dynamics equations as functions of an independent variable other than time [58][59][60]. In consequence, we choose the longitudinal distance, 𝑠, as the evolution variable and the time, 𝑡, becomes a state component.…”

Section: B Optimal Control Formulationmentioning

confidence: 99%

Holistic Approach for Aircraft Trajectory Optimization Using Optimal Control

Kasmi¹,

Laporte²,

Mongeau³

et al. 2023

Journal of Aircraft

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This paper deals with an optimal control approach for commercial aircraft trajectory planning, focusing on the vertical path and the minimization of the fuel burned. The main contribution is the optimization of the whole trajectory, also called the mission, without prior separation into different flight phases (climb, cruise, and descent), contrary to traditional approaches that consider the flight phases sequentially. The proposed optimal control problem (OCP) is formulated and then solved using a direct collocation method. Initial results yield optimal trajectories with a gradual climb during the cruise (climb cruise), which correspond to theoretical trajectories that minimize fuel consumption. Furthermore, a penalization is introduced to ensure vertical profiles featuring horizontal cruise levels on so-called flight levels, compliant with current air traffic management regulations. The use of direct methods to address this OCP induces large nonlinear optimization problems that are solved using an interior point method. This methodology is likely to lead to poor local optima, but a simple multistart heuristic shows that the solutions found for standard problems appear to be globally optimal. Such an approach does not need to impose a priori the cruise flight levels and is suitable for commercial aircraft flight planning purposes. It leads to fuel saving and subsequently to important improvements against environmental impact by reducing [Formula: see text] emissions.

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“…In this study, the goal is to have a very fast algorithm but one that is adaptable to any type of emergency situation. Hong et al [11] propose a computationally efficient method to generate a smooth level change in trajectory. Their proposed algorithm consists of a line search method in combination with a fixed-horizon sequential convex optimization method.…”

Section: Trajectory Generation Algorithmmentioning

confidence: 99%

An Accelerated Dual Fast Marching Tree Applied to Emergency Geometric Trajectory Generation

2022

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This paper addresses the generation of aircraft emergency trajectories with obstacle avoidance. After presenting in detail the fast marching tree algorithm, in this paper we propose an improvement of its performance. First, the free space checking function is sped up. Then, the algorithm is used twice, firstly with the sampling of a few points to generate an approximate trajectory, and secondly with a sampling of points close to the first computed trajectory to refine it. The proposed method significantly reduces the computing time of the emergency geometric trajectory generation.

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Computationally Efficient Trajectory Generation for Smooth Aircraft Flight Level Changes

Cited by 12 publications

References 21 publications

Flight Planning Optimization of Multiple UAVs for Internet of Things

Flight Planning Optimization of Multiple UAVs for Internet of Things

Holistic Approach for Aircraft Trajectory Optimization Using Optimal Control

An Accelerated Dual Fast Marching Tree Applied to Emergency Geometric Trajectory Generation

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