Conflict-Free Trajectory Optimization with Target Tracking and Conformance Monitoring

Vilardaga, Santi; Prats, Xavier; Duan, Pengfei; Haag, Maarten Uijt de

doi:10.2514/1.c034251

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…The positioning accuracy is low, and the detection effect of small objects is poor. In order to overcome the above problems, researchers have proposed improved versions of YOLOv2 and YOLOv3 [23,24]. The comparison is shown in Table 2.…”

Section: Experimental Model and Frameworkmentioning

confidence: 99%

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

Tang

Chen

Wang

et al. 2021

Wireless Communications and Mobile Computing

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With the development of information technology in the network era and the popularization of the 5G era, UAV-related applications are becoming more and more widely used, which is one of the essential basic technologies. Therefore, the technology has great research value and practical significance, a multiobjective detector based on support vector machine (SVM) is designed based on directional gradient histogram (HOG), and the startup method used with cross-validation methods can improve detector performance. It makes the detector accuracy above 98% and has good resistance to the target scale. A real-time target tracker is designed with its rotation variation and with an improved average displacement algorithm. The algorithm must manually select the target model and suggest the target model to achieve automatic acquisition of the target model. Due to the ambiguity of the target tracking state, several judgment conditions are set to determine whether the tracking has failed and whether the tracker state is correctly verified, with several similar target tracking algorithms. When the system is started, the system detects targets frame by frame. And it will locate a possible target by color segmentation and specify the target to be tracked to recommend the relevant model during the tracking process and open the tracker to determine the target tracking state frame by frame and perform target detection at each frame. Then it will find possible goals and will follow them to achieve a balance of stable and real-time system performance, using the results of the TPD-KCF method. The percentage of correctly tracking images can reach 98%, and the efficiency is significantly improved.

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Section: Experimental Model and Frameworkmentioning

confidence: 99%

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

Tang

Chen

Wang

et al. 2021

Wireless Communications and Mobile Computing

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“…The most advanced RNP procedures currently allow separation from obstacles as little as 0.2 NM in IMC [17]. Future implementations of Trajectory Based Operations (TBO) [18] or "free flight" operations [19], [20] may enable aircraft to operate on dynamically defined 4D flight trajectories with similarly reduced containment boundaries.…”

Section: Separation Standard Mechanismmentioning

confidence: 99%

Scaling Constraints for Urban Air Mobility Operations: Air Traffic Control, Ground Infrastructure, and Noise

Vascik

Hansman

2018

2018 Aviation Technology, Integration, and Operations Conference

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The scalability of the current air traffic control system, the availability of aviation ground infrastructure, and the acceptability of aircraft noise to local communities have been identified as three key operational constraints that may limit the implementation or growth of Urban Air Mobility (UAM) systems. This paper identifies the primary mechanisms through which each constraint emerges to limit the number of UAM operations in an area (i.e. the scale of the service). Technical, ecosystem, or operational factors that influence each of the mechanisms are also identified. Interdependencies between the constraints are shown. Potential approaches to reduce constraint severity through adjustments to the mechanisms are introduced. Finally, an effort is made to characterize the severity of each operational constraint as a function of the density of UAM operations in a region of interest. To this end, a measure of severity is proposed for each constraint. This measure is used to notionally display how the severity of the constraint responds to UAM scaling, and to identify scenarios where efforts to relieve the constraint are most effective. The overall purpose of this paper is to provide an abstraction of the workings of the key UAM operational constraints so that researchers, developers, and practitioners may guide their efforts to mitigation pathways that are most likely to increase achievable UAM system scale. U 2 American Institute of Aeronautics and Astronautics perceive to degrade their quality of life and enjoyment of their property. Local governments may use zoning or building codes to limit or prohibit aviation infrastructure development or operations. Finally, congressional representatives may compel the FAA to change ATC procedures to reduce noise, as was the case in the Los Angeles Residential Helicopter Noise Relief Act of 2013 [4]. While community acceptance of UAM activities may be influenced by numerous factors including privacy, viewshed, pollution, safety, and equity, aircraft noise has dominated recent public discourse and action and represents a key constraint for UAM operations. However, the successful implementation of helicopter-based UAM operations in São Paulo, as well as the large private and charter helicopter operations in Moscow and Mumbai, indicate that local values and expectations significantly influence the actual severity of this constraint. U.S. and European communities may exhibit increased sensitivity to aircraft noise compared to other potential UAM markets.These three operating constraints are of keen interest to researchers, potential operators, and transportation planners as they dictate if a UAM system may serve a few hundred customers per day or tens of thousands of customers per day. The contribution of this research is significant because it will support the estimation of achievable UAM system scale based upon local factors and the ecosystem of the city in which it is implemented. Furthermore, this work may support the evaluation of how potential design and operating decisi...

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“…This decomposition is historically justified by the structure of aircraft systems (aerodynamics configurations, engine ratings) and also by the operational procedures that change from phase to phase. Some research studies consider the whole aircraft trajectory, and model the problem as a multi-phase trajectory optimization problem [23][24][25][26][27]. A major difficulty of addressing such a multi-phase optimization problem is the management of the transition between phases, which is classically modeled with large-scale mixed-integer nonlinear optimization problems involving thereby prohibitive combinatorics.…”

Section: Introductionmentioning

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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Conflict-Free Trajectory Optimization with Target Tracking and Conformance Monitoring

Cited by 6 publications

References 23 publications

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

Scaling Constraints for Urban Air Mobility Operations: Air Traffic Control, Ground Infrastructure, and Noise

Holistic Approach for Aircraft Trajectory Optimization Using Optimal Control

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