Optimal Flight Research Articles

Performance Improvement of Hierarchical Fitness Calculation for Optimal Flight Trajectory Generation Using Dynamically Distributed Genetic Algorithm

Performance Improvement of Hierarchical Fitness Calculation for Optimal Flight Trajectory Generation Using Dynamically Distributed Genetic Algorithm

Post-Disaster Unmanned Aerial Vehicle Base Station Deployment Method Based on Artificial Bee Colony Algorithm

Ground-based communication facilities are at risk of being destroyed after natural disasters, such as earthquakes, floods, tsunamis, hurricanes, fires, or terrorist attacks. Unmanned aerial vehicles (UAV) can be used as air base stations to support user equipment (UE). UAV base station (UAV-BS) development plays a major part in rescue operations and post-disaster reconstruction. Improving network throughput in the UAV-BS signal coverage area and reducing deployment costs while maintaining effective communication are important issues that need to be addressed. In view of the problem, this paper demonstrates the problem of maximizing network throughput under the constraint of UAV-BS capacity by deploying UAV-BS. We proposed a UAV-artificial bee colony (U-ABC) algorithm to solve the problem of UAV-BS deployment. U-ABC algorithm can calculate the optimal flight position of each UAV-BS and maximum network throughput in the disaster area. In performance evaluation, we compared U-ABC algorithm with genetic algorithm, Greedy-ABC algorithm, PSO algorithm, DI-PSO algorithm and PSO-GWO algorithm. We analyzed the flight height altitude of UAV-BSs, the interference factor of UAV-BS, and the influence of the number of UE on network throughput. Results show that the proposed method improved the overall network throughput and achieved a high UE coverage rate under a given number of UAV-BSs.

Heterogeneous UAV Cells: An Effective Resource Allocation Scheme for Maximum Coverage Performance

This paper develops an effective approach for the 3D deployment of a heterogeneous set of unmanned aerial vehicles (UAVs) acting as aerial base stations that provide maximum wireless coverage for ground users in a given geographical area. This problem is addressed in two steps. First, in order to maximize the utilization of each UAV, its optimal flight altitude is found based on the UAV's transmit power which provides maximum coverage radius on the ground. The UAVs are classified into separate groups based on their transmit powers and optimal flight altitudes. Next, given a repository of UAVs belonging to different classes, the proposed technique finds an optimal subset of the available UAVs along with their optimal 3D placement to provide the maximum network coverage for a given area on the ground with the minimum power consumption. This optimization problem is proved to be NP-hard, for which a novel algorithm is proposed to solve the problem. Simulation results demonstrate the effectiveness of the proposed solution and provide valuable insights into the performance of the Heterogeneous UAV-supported small cell networks.

Optimal civil aircraft missions exploiting free routing possibilities

A method is presented for optimizing burnt fuel and flight time of civil aircraft missions, under the scope of a full-scale free route airspace implementation as well as usage of actual environmental data, focusing on ambient temperature and pressure. A computational analysis model, using online available aircraft data is considered, for flight envelopes defined by the user, is used as the base tool. The model is validated over real flight data, before it is used as the main tool for optimization of flight missions. Flight paths are analyzed by considering both their vertical and horizontal aspect. Optimal paths are derived, for each chosen mission, through an optimization process that takes advantage of the flexibilities that become available by implementation of Free Route Airspace. Vertical flight path is shown to play very important role in achieving optimal flights, while horizontal paths also offer optimality possibilities, with a strong dependence on weather conditions.

Sposób wyboru optymalnej trajektorii lotu manewru antykolizyjnego realizowanego w otoczeniu ruchomych przeszkód

Sposób wyboru optymalnej trajektorii lotu manewru antykolizyjnego realizowanego w otoczeniu ruchomych przeszkód

Airport congestion delays and airline networks

This paper investigates the role of airport congestion delays in the network choice of a monopoly airline. By comparing a hub-and-spoke network (HS), a point-to-point network (PP) and a mixed network (MX), we find the following. First, the optimal total flight frequency may be higher under HS than PP. Moreover, because of the inclusion of congestion delays, the optimal aircraft size is smaller under HS than PP, and the airline may choose PP even when the extra travel time disutility of one-stop services is relatively low. Finally, welfare analysis shows the airline’s inefficient bias towards PP.

Analysis of control programs and flight paths of a hypersonic vehicle in climb

Control programs and flight paths of a hypersonic vehicle in climb are analyzed. Two approaches to solving the task of determining the control programs and flight paths are identified: the traditional approach and the optimization one. The traditional approach implies specifying a typical mission profile of a hypersonic vehicle. In order to maximize the specific impulse (optimize the performance of the propulsion system) the vehicle moves along the line of peak dynamic pressing. In the case of the optimization approach the minimum fuel problem is stated and solved using the method of Pontryagins maximum principle. It concerns the mass of fuel consumed in hypersonic acceleration. Optimal control programs and optimal flight paths are determined. The results of modeling the motion of a hypersonic vehicle with angle-of-attack schedules corresponding to the traditional and optimization approaches are presented and discussed. It is established that less fuel is consumed in the case of optimal control, which is accounted for by more efficient use of the hypersonic vehicle aerodynamic performance due to direct control of the angle of attack.

Хвильовий метод визначення траєкторій БПЛА для ефективного спостереження за ділянкою місцевості

The paper considers the application of the wave method to search for the optimal UAV flight trajectories in a three-dimensional space concerning observation over the border sector. An appropriate efficiency indicator has been used in the process of constructing a three-dimensional mask of a modified wave algorithm that describes the airspace points in view of their effectiveness for the border sector observation. This indicator takes into account a set of factors that significantly affect the resulting observation effectiveness: the impedance of terrain and vegetation, the resolution of the UAV camera, the influence of observation conditions. A method of discretization of the investigated space in which the task is performed has been proposed in order to reduce the computational complexity of a wave algorithm, which significantly increases with enlarging the dimension of space in which wave propagation is simulated. Usage of the proposed approach to discretization significantly reduces the power of the plurality of points the wave method influences at.

Fast generation of initial estimates for indirect optimal control

AbstractFor the numerical solution of optimal control problems for large systems of ordinary differential equations (ODE) the indirect approach can be applied: Using optimal control theory based on the Maximum Principle the problem is transformed into a boundary value problem which is then solved numerically. In addition to the equations of motion, a set of differential equations for the adjoint variables is derived from the Hamiltonian. A main challenge for indirect methods was up to now the generation of initial estimates for the adjoint variables. A new approach is presented based on the estimated or experimentally observed controls of a suboptimal solution. They are formally integrated backwards; together with the algebraic constraints on optimal solutions (optimality condition and constant Hamiltonian) an inverse problem is formulated and solved to obtain reliable estimates for the adjoint variables on a predefined grid. By homotopy techniques the so obtained optimal solution of a modified problem is transferred into the solution of the original problem. As an example the optimal flight trajectory of an aerobatic aircraft in an Red Bull Air Race is calculated.

Should I stay, or should I go: Modeling optimal flight initiation distance in nesting birds.

Flight initiation distance (FID)–the distance at which an individual leaves in response to the approach of a perceived threat–provides a measurement of risk-taking behavior. If individuals optimize their FID, this distance should reflect the point at which the fitness resulting from leaving exceeds the fitness resulting from all other possible decisions. Previous theory of FID has often been aimed at explaining this behavior in foraging individuals. Yet flight initiation in response to approaching threats occurs in a range of contexts that might influence optimal behavior. In breeding individuals, risk-taking decisions that are made at a location of offspring care (e.g., a nest or den) can have significant effects on fitness. Here, we develop a theoretical model of distances at which a parent bird flushes from a nest in response to an approaching threat. We estimate parent fitness with regards to characteristics of the parent (reproductive values, detection distance, and cost of lost parental care cost), the nest (concealment and accessibility), and the approaching predator (detection capability and predation success), developing a dichotomous scenario between staying at the nest or leaving at varying distances. Using a generalized comparison of the benefits of leaving versus staying, we find that increasing costs of lost parental care, probability of predation of the parent due to fleeing, or current reproductive value lead to more instances of staying at the nest. In a complementary approach with specified parameters based on biologically-informed factors that likely influence a predator-prey encounter, we find that increasing the current reproductive value, concealment of the nest, or costs of lost parental care decrease optimal FID and can lead to the parent staying at the nest. Other factors, such as increasing residual reproductive value, predation success, and predator capability of detecting the nest, increase optimal FID with some instances of costs of fleeing being so great that staying becomes an optimal strategy. Our theory provides a framework to explain variation in FID among nesting species and individuals and could provide a foundation for future empirical investigations of risk-taking behavior.

UAS imagery reveals new survey opportunities for counting hippos.

IntroductionThe common hippopotamus Hippopotamus amphibius L. is a vulnerable species that requires efficient methods to monitor its populations for conservation purposes. Rapid evolution of civil drones provides new opportunities but survey protocols still need development. This study aims to determine the optimal flight parameters for accurate population estimates. A second objective is to evaluate the effects of three environmental factors: wind speed, sun reflection and cloud cover.MethodWe estimated the population of two main hippo schools (Dungu and Wilibadi II) located in Garamba National Park in Democratic republic of Congo. Eight observers reviewed 252 photos taken over the Dungu school, representing a total of 2016 experimental units. A detection rate and a level of certainty were associated with each experimental unit, and five parameters were related to each count: flight height, three environmental parameters (sun reflection on water surface, cloud cover, and wind speed), and observers’ experience.ResultsFlight height reduced the observers’ confidence in their detection ability, rather than the detection itself. For accurate counts of large groups an average height of 150 m was shown to be a good compromise between animal detection without zooming in and the area covered in one frame. Wind speed had little influence on the counts, but it affected the performance of the UAS. Sun reflection reduced the detection rate of hippos and increased level of certainty, while cloud cover reduced detection rates slightly. Therefore, we recommend flying when the sun is still low on the horizon and when there is little cloud, or when cloud cover is light and even. This last point reinforces our recommendation for flights early in the day. The counts also showed large differences between groups of inexperienced and experienced observers. Experienced observers achieved better detection rates and were generally more confident in their detection. Experienced observers detected 86.5% of the hippos on average (confidence interval = ±0.76%). When applied to data from the second site, the detection was 84.3% (confidence interval = ±1.84%). Two correction factors were then calculated, as the inverse of the detection rate, based on the estimated number of hippos present during one flight (Factor 1) or in the general population respectively (Factor 2). Factor 2 especially was consistent with previous studies using traditional aerial counts (1.22 vs 1.25). Factor 2 was found to be appropriate for use by experienced observers. These results confirm the use of correction factor 2 for hippo surveys, regardless of the study site, as it accounts for hippo behavior. Optimum counting and cost efficiency were achieved with two trained observers counting 7 pictures.ConclusionThis study is a promising approach for routine surveys of the hippopotamus which is a species usually ignored in wildlife counts. Drone technology is expected to improve rapidly; therefore UAS could become a very useful and affordable survey tool for other species requiring specific monitoring.

Multiview Synthetic Aperture Radar Automatic Target Recognition Optimization: Modeling and Implementation

Multiview synthetic aperture radar (SAR) images could provide much richer information for automatic target recognition (ATR) than from a single-view image. It is desirable to find optimal SAR platform flight paths and acquire a sequence of SAR images from appropriate views, so that multiview SAR ATR can be carried out accurately and efficiently. In this paper, a novel optimization framework for multiview SAR ATR is proposed and implemented. The geometry of the multiview SAR ATR is modeled according to the recognition mission and flight environment. Then, the multiview SAR ATR is abstracted and transformed into a constrained multiobjective optimization problem with objective functions considering the tradeoffs between recognition performance and efficiency and security. A specific approach based on convolutional neural network ensemble and constrained nondominated sorting genetic algorithm II is employed to solve the multiobjective optimization, and optimal flight paths and corresponding imaging viewpoints are obtained. The SAR sensor can thus choose an applicable flight path to acquire the multiview SAR images from different tradeoff solutions according to application requirements. Finally, accurate recognition results can be obtained based on those multiview SAR images. Extensive experiments have shown the validity and superiority of the proposed optimization framework of multiview SAR ATR.

Aerodynamics characteristics of glider GL-1 based on computational fluid dynamics

Glider GL-1 was especially designed for thermal updraft condition of Indonesia. The development of GL-1 is the first in Indonesia to design a glider for aero sport purpose in cooperation with FASI. This glider needs a minimum aerodynamics efficiency of 8.333 to meet design requirement derived from thermal updraft condition, which needs rate of descent little than 3 m/s at gliding angle of 2 degree. Optimum flight condition for maximum range performance has been calculated to be 25 m/s at a condition of altitude between 300 – 2000 m with Reynolds number of 1 – 1.5 million. Computational Fluid Dynamics (CFD) was employed to do numerical analyses to predict aerodynamics characteristics of the glider. CFD by using half-glider meshing results maximum lift coefficient of 1.326 at angle of attack of 8 degree, and maximum aerodynamics efficiency of 19 at angle of attack 2 degree. The result of CFD by using full-glider meshing gives maximum lift coefficient of 1.2556 at angle of attack of 10 degree and maximum aerodynamics efficiency of 16 at angle of attack of 2 degree. Both of half-glider meshing and full-glider meshing are employing k-ε turbulence model. Comparison with preliminary design result and benchmarking with similar gliders data was also done.

Optimization of an aircraft flight trajectory in the GLONASS dynamic accuracy field

Advanced technologies in air traffic management assume the transition to flexible routing based on the use of the satellite navigation systems. However, the accuracy of these systems depends on the location of the navigation satellites in relation to the target object and will vary in the available airspace. Therefore, the designed optimal flight path of the aircraft should be built taking into account the accuracy of its keeping in the variable navigation-time field (accuracy field) of the satellite navigation system. The accuracy field of the satellite navigation systems can be characterized by the geometric factor (spatial, horizontal and vertical). The geometric factor of the satellite navigation system is determined by the relative position of the consumer and the satellites upon which the navigation problem is solved, and is a deterministic value. Due to the orbital motion of satellites and the movement of the consumer, the geometric factor will change in space and time. Knowing the laws of the satellites orbital motion it is possible to calculate the geometric factor for any point in the air space and for any moment of time according to the known almanac of the system. This allows predicting the expected accuracy of the navigation and time determination during the flight on a particular air route. Optimization methods based on the algorithms of A-star and Dijkstra graph theory are chosen for aircraft flight trajectories construction. Mathematical modeling is used for the optimal trajectory construction in the GLONASS dynamic accuracy fields with their various structures in static and dynamic problem setting.

Formulation of multicriteria problem of routing and scheduling of manned and unmanned aircraft in a dynamic environment and approach to its solution using genetic algorithms

The article deals with the multi-criteria task of routing and scheduling of unmanned and manned aircraft using the method of penalty functions. The authors describe the urgency of the problem being solved for the airline management under the conditions of the existing changes in the dynamic situation with a great variety of flight situations. A mathematical statement of the problem is formulated and a universal optimality criterion is proposed in the form of a sum of additive and multiplicative forms, including partial quality indicators. The search for optimal and rational solutions to the problem of optimal flight routing, taking into account the airline fleet resources, airspace users' offers, constant and variable restrictions, associated, for example, with unfavorable weather conditions, can be implemented using a one-criteria and multi-criteria approach, but as a result, it is proposed to use a genetic algorithm that has low computational complexity and offers as solutions ("ancestors"), close to the optimal and rational result. With this approach, an "elite" is formed at the beginning of the algorithm, which allows the crossing operation to be performed at each step of the iteration (evolution). As a result, we get new "descendants", and by rearranging at least one item from one block to the next one, we can get a sufficiently large number of representatives, from which a new "elite" can be selected with the help of the criterion. The practice of using genetic algorithms has shown that along with the achievement of a global extremum with it, the process of substantial improvement of planning results is achieved in several evolution steps, and their number is clearly less than the number of steps when using numerical methods of parametric optimization. The proposed approach will significantly improve the efficiency and quality of flight planning for the airline, taking into account the diversity of aircraft fleet, payload and environmental impact. This task is especially important under the conditions of joint management (CDM), where aeronautical data can be considered as additional criteria.

Об одной задаче оптимального оперативного управления движением воздушных судов авиакомпании

Об одной задаче оптимального оперативного управления движением воздушных судов авиакомпании

DroneTank: Planning UAVs' Flights and Sensors' Data Transmission under Energy Constraints.

We consider an Unmanned Aerial Vehicle (UAV, also known as drone) as an aerial sink to travel along a natural landscape or rural industrial linear infrastructure to collect data from deployed sensors. We study a joint schedule problem that involves flight planning for the drone and transmission scheduling for sensors, such that the maximum amount of data can be collected with a limited individual energy budget for the UAV and the sensors, respectively. On one hand, the flight planning decides the flight speed and flight path based on sensor locations, energy budgets, and the transmission schedule. On the other hand, the transmission schedule decides for each sensor when to deliver data and what transmission power to use based on the energy budgets and flight plan. By observing three import optimality properties, we decouple the joint problem into two subproblems: drone flight planning and sensor transmission scheduling. For the first problem, we propose a dynamic programming algorithm to produce the optimal flight planning. For the second problem, with a flight plan as input, we introduce a novel technique (water-tank), which together with dynamic programming, is the key to achieve an optimal transmission schedule that maximizes data collection. Simulations show that the separately determined flight plan and transmission schedule are near-optimal for the original joint problem.

Time Guidance in the Navigational Complexes of Aircraft

Further increase in airspace capacity and flight efficiency makes it expedient to introduce four-dimensional flight trajectories, as well as operations based on their use, in order to maintain a high level of safety. At the same time, the implementation of control systems that support maintenance of four-dimensional trajectories faces some problems; the major one is to choose the optimal flight mode. An additional fourth dimension, that is represented by time, imposes some restrictions on the classical three-dimensional control, which, accordingly, entails a deviation from the optimal and cost-effective three-dimensional trajectory. The article proposes a variant whereby the time guidance loop is separated by the major loop of the flight management system in the horizontal and in the vertical planes. Guidance is carried out by flight simulation along a designated route, taking into account the aircraft performance characteristics in varying environmental parameters conditions. A method of time guidance is presented; it provides a generation of preventive recommendations for a flight mode change in order to maintain the flight watch. These recommendations allow the crew to make a preliminary assessment of the feasibility of compliance with a target time of arrival, as well as an analysis, a required path correction and flight parameter changes in order to fulfill these conditions.

An Optimization Problem for Quadcopter Reference Flight Trajectory Generation

This paper deals with the reference flight trajectory generation and planning problems for quadcopter Unmanned Aerial Vehicle (UAV). The reference flight trajectory is defined as the composition of path and motion functions. Both of them are generated by using quintic B-spline functions. Based on differential flatness approach, the quadcopter dynamical constraints are satisfied instantaneously by computing the induced aerodynamical moments and lift force. The optimal reference flight trajectory, with respect to the mission requirements and imposed constraints, is reached by manipulating the control points’ vectors of B-spline functions via a nonlinear constrained optimization method. The mission requirements are defined as a set of waypoints with their respective scheduled flight timetable. A minimum-energy cost function is developed to minimize the consumed energy and induced efforts by reference flight trajectory. For the need of the optimal overfly-times schedule, the overfly times with respect to the defined constraints and performance criteria are calculated. Numerical simulation results show the feasibility and effectiveness of the proposed optimization method.

DIMPL: a distributed in-memory drone flight path builder system

Drones are increasingly being used to perform risky and labor intensive aerial tasks cheaply and safely. To ensure operating costs are low and flights autonomous, their flight plans must be pre-built. In existing techniques drone flight paths are not automatically pre-calculated based on drone capabilities and terrain information. Instead, they focus on adaptive shortest paths, manually determined paths, navigation through camera, images and/or GPS for guidance and genetic or geometric algorithms to guide the drone during flight, all of which makes flight navigation complex and risky. In this paper we present details of an automated flight plan builder DIMPL that pre-builds flight plans for drones tasked with surveying a large area to take photographs of electric poles to identify ones with hazardous vegetation overgrowth. The flight plans are built for subregions allowing the drones to navigate autonomously. DIMPL employs a distributed in-memory paradigm to process subregions in parallel and build flight paths in a highly efficient manner. Experiments performed with network and elevation datasets validated the efficiency of DIMPL in building optimal flight plans for a fleet of different types of drones and demonstrated the tremendous performance improvements possible using the distributed in-memory paradigm.