Flight Trajectory Of Unmanned Aerial Vehicle Research Articles

Completion Time Optimization in UAV-Relaying-Assisted MEC Networks With Moving Users

As one of the most popular consumer electronics, Unmanned Aerial Vehicle (UAV) has the potential to assist User Equipment (UE) in the Internet of Everything. Mobile edge computing (MEC) is also an emerging technique that can provide sufficient computing resources to IoE users. In this paper, we focused on the UAV-assisted MEC problem with mobile UEs, where the UAV serves as an MEC server to assist UEs in computing and acts as a relay to deliver tasks to a ground access point (AP). The objective is to minimize the average time for completing all tasks in the network while jointly optimizing resource allocation such as communication bandwidth, CPU frequency, task division ratio, and UAV’s three-dimensional location deployment. First, we proposed an online MEC network adjustment scheme. Then, we decomposed the formulated non-convex problem into three low-complexity subproblems. Last, we proposed a successive convex approximation-based joint optimization algorithm to solve them. In addition, we presented heuristic conclusions in task allocation rules, UAV flight trajectory prediction, and UAV hovering altitude selection, which can further reduce task completion time and deepen the understanding of the working mode of mobile edge servers. Simulation results show that the algorithm can significantly reduce the completion time.

Joint Trajectory Design and Resource Allocation for Secure Air–Ground Integrated IoT Networks

We investigate in this paper joint trajectory design and resource allocation for secure air-ground integrated Internet of Things (IoT) networks with unmanned aerial vehicle (UAV) jamming and device to device (D2D) enhancement. By jointly optimizing ground user scheduling, UAV flight trajectory and transmit power, we are able to maximize the minimum system secrecy rate of UAV and D2D communications. The formulated optimization problems of the two typical network scenarios, i.e., with and without UAV jamming, are challenging to be solved due to the corresponding non-smooth and non-concave objective functions. Therefore, we propose alternating iterative algorithms to solve the problems by employing the successive convex approximation and block coordinate descent methods. Extensive results indicate that the proposed joint optimization schemes can effectively improve the secrecy communication performance under different spatial distributions of ground users.

UAV Trajectory and Energy Efficiency Optimization in RIS-Assisted Multi-User Air-to-Ground Communications Networks

An air-to-ground downlink communication network consisting of a reconfigurable intelligent surface (RIS) and unmanned aerial vehicle (UAV) is proposed. In conjunction with a resource allocation strategy, the system’s energy efficiency is improved. Specifically, the UAV equipped with a RIS starts from an initial location, and an energy-efficient unmanned aerial vehicle deployment (EEUD) algorithm is deployed to jointly optimize the UAV trajectory, RIS phase shifts, and BS transmit power, so as to obtain a quasi-optimal deployment location and hence improve the energy efficiency. First, the RIS phase shifts are optimized by using the block coordinate descent (BCD) algorithm to deal with the nonconvex inequality constraint, and then integrated with the Dinkelbach algorithm to address the resource allocation problem of the BS transmit power. Finally, for solving the UAV trajectory optimization problem, the complex objective function is transformed into a convex function, and the optimal UAV flight trajectory is obtained. Our simulation results show that the quasi-optimal deployment location obtained by the EEUD algorithm is superior to other deployment strategies in energy efficiency. Moreover, the instantaneous energy efficiency of the UAVs along the trajectory of searching the deployment location is better than other comparison trajectories. Furthermore, the RIS-assisted multi-user air-to-ground communication network can offer up to 145% improvement in energy efficiency over the traditional amplify-and-forward (AF) relay.

Two-Step Approach toward Alignment of Spatiotemporal Wide-Area Unmanned Aerial Vehicle Imageries

Recently, analysis and decision-making based on spatiotemporal unmanned aerial vehicle (UAV) high-resolution imagery are gaining significant attention in smart agriculture. Constructing a spatiotemporal dataset requires multiple UAV image mosaics taken at different times. Because the weather or a UAV flight trajectory is subject to change when the images are taken, the mosaics are typically unaligned. This paper proposes a two-step approach, composed of global and local alignments, for spatiotemporal alignment of two wide-area UAV mosaics of high resolution. The first step, global alignment, finds a projection matrix that initially maps keypoints in the source mosaic onto matched counterparts in the target mosaic. The next step, local alignment, refines the result of the global alignment. The proposed method splits input mosaics into patches and applies individual transformations to each patch to enhance the remaining local misalignments at patch level. Such independent local alignments may result in new artifacts at patch boundaries. The proposed method uses a simple yet effective technique to suppress those artifacts without harming the benefit of the local alignment. Extensive experiments validate the proposed method by using several datasets for highland fields and plains in South Korea. Compared with a recent work, the proposed method improves the accuracy of alignment by up to 13.21% over the datasets.

Energy-efficient full-duplex UAV relaying with trajectory optimization and power control in maritime communication environments

This paper solves an energy-efficient optimization problem of a fixed-wing unmanned aerial vehicle (UAV) assisted full-duplex mobile relaying in maritime communication environments. Taking the speed and the acceleration of the UAV and the information-causality constraints into consideration, the energy-efficiency of the system under investigation is maximized by jointly optimizing the UAV's trajectory and the individual transmit power levels of the source and the UAV relay nodes. The optimization problem is non-convex and thus cannot be solved directly. Therefore, it is decoupled into two subproblems. One sub-problem is for the transmit power control at the source and the UAV relay nodes, and the other aims at optimizing the UAV's flight trajectory. By using the Lagrangian dual and Dinkelbach methods, the two sub-problems are solved, leading to an iterative algorithm for the joint design of transmit power control and trajectory optimization. Computer simulations demonstrated that by conducting the proposed algorithm, the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions, and the proposed algorithm can achieve significantly higher energy efficiency as compared with the other benchmark schemes.

Joint Deployment Optimization and Flight Trajectory Planning for UAV Assisted IoT Data Collection: A Bilevel Optimization Approach

This work investigates an unmanned aerial vehicle (UAV) assisted IoT system, where a UAV flies to each foothold to collect data from IoT devices, and then return to its start point. For such a system, we aim to minimize the energy consumption by jointly optimizing the deployment and flight trajectory of UAV. It is a mixed-integer non-convex and NP-hard problem. In order to address it, a bilevel optimization approach is proposed, where an upper-level method aims to optimize the deployment of UAV and a lower-level one aims to plan UAV flight trajectory. Specifically, the former optimizes the number and locations of footholds of UAV. This work proposes an improved dandelion algorithm with a novel encoding strategy, in which each dandelion represents a foothold of UAV and the entire dandelion population is seen as an entire deployment. Then, two mutation strategies are designed to adjust the number and locations of footholds. Based on the footholds of the UAV provided by the former, the latter transforms flight trajectory planning into a traveling salesman problem (TSP). This work proposes an iterated greedy algorithm to solve it efficiently. The effectiveness of the proposed bilevel optimization approach is verified on ten instances, and the experimental results show that it significantly outperforms other benchmark approaches.

Anti-jamming trajectory design for UAV-enabled wireless sensor networks using communication flight corridor

This paper investigates the anti-jamming trajectory design to safeguard the effective data collection, where a unmanned aerial vehicle (UAV) is dispatched to collect data over multiple sensor nodes(SNs) in jamming environment. Under the limited power and transmission range of SNs, we aim to minimize the UAV's flight energy consumption in a finite task period, by jointly optimizing SNs collection sequence and UAV flight trajectory. Firstly, we propose a general optimization framework which consists of path planning and trajectory optimization for the formulated non-convex problem. In the path planning phase, a dynamic programming (DP) algorithm is used to provide the initial path of the UAV, which is the shortest path to visit each SN. In the trajectory optimization phase, we introduce the concept of Communication Flight Corridor (CFC) to meet the non-convex constraints and apply a piecewise Bezier curve, based on Bernoulli polynomial, to represent the flight trajectory of the UAV, which can transform the optimization variables from infinite time variables to polynomial coefficients of finite order. Finally, we simulate the flight trajectory of UAV in hovering mode and continuous flight mode under different parameters, and the simulation results demonstrate the effectiveness of the proposed method.

Flight Trajectories Optimization of Fixed-Wing UAV by Bank-Turn Mechanism

This paper addresses an optimization of Unmanned Aerial Vehicle (UAV) flight trajectories by bank-turn mechanism for a fixed-wing UAV at a constant altitude. The flight trajectories should be optimal and stay in the UAV flight operational area. The maneuver planning is conducted in two steps, which are UAV path planning and UAV flight trajectory planning. For the first step, the Bezier curve is employed as a maneuvering path. The path planning optimization objective is to minimize the path length while satisfying maximum curvature and collision avoidance constraints. The flight trajectories optimization objective is to minimize maneuvering time and load factor considering, minimum/maximum speed, minimum/maximum acceleration, maximum roll angle, maximum turn rate, and aerodynamics constraints. The variable speed trajectory generation is developed within allowable speed zone considering these UAV flight constraints by employing meta-heuristic optimizations. Results show that the PSO have outperformed the GA and the GWO for both steps of path planning and trajectory planning. The variable speed has succeeded in reducing the load factor during the bank-turn mechanism using the Bezier curve. The variable speed is recommended to be conducted when the result of the maneuvering path involve the lower turning radius. A simultaneous on arrival target mission has also succeeded to be conducted using the combination of the variable speed and constant speed strategies.

Joint deployment and trajectory optimization in UAV-assisted vehicular edge computing networks

As the general mobile edge computing (MEC) scheme cannot adequately handle the emergency communication requirements in vehicular networks, unmanned aerial vehicle (UAV)-assisted vehicular edge computing networks (VECNs) are envisioned as the reliable and cost-efficient paradigm for the mobility and flexibility of UAVs. UAVs can perform as the temporary base stations to provide edge services for road vehicles with heavy traffic. However, it takes a long time and huge energy consumption for the UAV to fly from the stay charging station to the mission areas disorderly. In this paper, we design a pre-dispatch UAV-assisted VECNs system to cope with the demand of vehicles in multiple traffic jams. We propose an optimal UAV flight trajectory algorithm based on the traffic situation awareness. The cloud computing center (CCC) server predicts the real-time traffic conditions, and assigns UAVs to different mission areas periodically. Then, a flight trajectory optimization problem is formulated to minimize the cost of UAVs, while both the UAV flying and turning energy costs are mainly considered. In addition, we propose a deep reinforcement learning(DRL)-based energy efficiency autonomous deployment strategy, to obtain the optimal hovering position of UAV at each assigned mission area. Simulation results demonstrate that our proposed method can obtain an optimal flight path and deployment of UAV with lower energy consumption.

A Novel Sparrow Particle Swarm Algorithm (SPSA) for Unmanned Aerial Vehicle Path Planning

Unmanned aerial vehicle (UAV) has been widely used in various fields, and meeting practical high-quality flight paths is one of the crucial functions of UAV. Many algorithms have the problem of too fast convergence and premature in UAV path planning. This study proposed a sparrow particle swarm algorithm for UAV path planning, the SPSA. The algorithm selects a suitable model for path initialization, changes the discoverer position update, and reinforces the influence of start-end line on path search, which can significantly reduce blind search. The number of target points reached is increased by adaptive variable speed escapes in areas of deadlock. In this case, the planned trajectory will fluctuate, and adaptive oscillation optimization can effectively reduce the fluctuation of the path. Finally, the optimal path is simplified, and the path nodes are interpolated with cubic splines to improve the smoothness of the path, which improves the smoothness of the UAV flight trajectory and makes it more suitable for use as the UAV real flight trajectory. By comparison, it can be concluded that the improved SPSA has good convergence speed and better search results and can avoid local optimality.

Analytical Synthesis of Control Acceleration of Unmanned Aerial Vehicle

. The problem of analytical synthesis of the control acceleration for an unmanned aerial vehicle (UAV) during its flight along a complex trajectory, consisting of sequentially located horizontal flight sections, located at different heights relative to the earth's surface has been solved in the paper. The problem has been solved as an analytical definition of the optimal control of a linear non-stationary system for a specified minimized quality functional. The mathematical model of the system is presented in the form of differential equations of UAV motion in the vertical plane of a fixed coordinate system related to the earth's surface. A feature of the proposed methodology for solving the problem is the substantiation of the original form of the minimized functional and parameters included in the law of variation of the control acceleration obtained by known methods. As the components of the quality functional, the values of coordinates and velocity of the UAV are considered and they are specified at the corresponding points in space through which the UAV path must pass, in order to obtain the optimal curvature of the trajectory. The derived mathematical dependences make it possible to implement them on board of an aircraft and, ultimately, solve the problem of ensuring the minimum energy consumption when controlling an object (UAV). Computer simulation of the analytically obtained results in the form of the UAV flight trajectory and the processes of changing its acceleration and speed have shown the efficiency of the proposed technique and the prospects of its use at the initial stage of the synthesis of the UAV control system.

TWO-LEVEL TECHNOLOGY OF INTELLIGENT APPLICATION OF ON-BOARD VIDEO CAMERA OF UNMANNED AERIAL VEHICLE FOR MONITORING OF GEOSPATIAL DATA

The main reasons for the deterioration in the quality of images obtained from an unmanned aerial vehicle (UAV) are wind disturbances, as well as a change of the position of the UAV during maneuvering. Traditional methods of trajectory stabilization used in robotics do not work well enough for UAVs. In this regard, there is an urgent task of developing algorithms for reducing the effect of wind disturbances and stabilizing the flight trajectory of UAVs, as well as creating methods for eliminating low quality images already obtained as a result of various types of disturbances.In this paper, a two-level technology is proposed. This technology determines the critical parameters of the movement of unmanned aerial vehicles, performs spatial compensation of the influence of wind disturbances in both the vertical and horizontal planes for these parameters, and improves the quality of the data stream of the on-board video camera by selecting high-quality images suitable for further processing.The first level of the proposed technology provides a decrease in the effect of wind disturbances on the UAV flight trajectory. The second level categorizes the flow of images of an onboard video camera for geospatial monitoring as a result of using a high-speed integral stability criterion, which eliminates in real time low quality images from the data flow from the onboard video camera.The developed system for intelligent control of UAVs and procedures for selecting images by color and sharpness make it possible to successfully solve problems that are largely similar to those that a human expert can face when solving intellectual problems of processing and filtering video information. Therefore, these methods, algorithms and procedures can be implemented in advanced systems of intelligent control in the field of modeling the conscious behavior of a person for the selection of data necessary to perceive the characteristics of the external environment.

Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR.

The paper presents the concept of planning the optimal trajectory of fixed-wing unmanned aerial vehicle (UAV) of a short-range tactical class, whose task is to recognize a set of ground objects as a part of a reconnaissance mission. Tasks carried out by such systems are mainly associated with an aerial reconnaissance using Electro-Optical/Infrared (EO/IR) systems and Synthetic Aperture Radars (SARs) to support military operations. Execution of a professional reconnaissance of the indicated objects requires determining the UAV flight trajectory in the close neighborhood of the target, in order to collect as much interesting information as possible. The paper describes the algorithm for determining UAV flight trajectories, which is tasked with identifying the indicated objectives using the sensors specified in the order. The presence of UAV threatening objects is taken into account. The task of determining the UAV flight trajectory for recognition of the target is a component of the planning process of the tactical class UAV mission, which is also presented in the article. The problem of determining the optimal UAV trajectory has been decomposed into several subproblems: determining the reconnaissance flight method in the vicinity of the currently recognized target depending on the sensor used and the required parameters of the recognition product (photo, film, or SAR scan), determining the initial possible flight trajectory that takes into account potential UAV threats, and planning detailed flight trajectory considering the parameters of the air platform based on the maneuver planning algorithm designed for tactical class platforms. UAV route planning algorithms with time constraints imposed on the implementation of individual tasks were used to solve the task of determining UAV flight trajectories. The problem was formulated in the form of a Mixed Integer Linear Problem (MILP) model. For determining the flight path in the neighborhood of the target, the optimal control algorithm was also presented in the form of a MILP model. The determined trajectory is then corrected based on the construction algorithm for determining real UAV flight segments based on Dubin curves.

Towards minimum code dissemination delay through UAV joint vehicles for smart city

In this study, an update code using vehicles joint unmanned aerial vehicle (UC-VU) scheme is proposed to disseminate code for smart sensing devices (SSDs) in the smart city. The innovations of the UC-VU scheme are as follows. (i) The unmanned aerial vehicle (UAV) is adopted to disseminate code at the beginning of code dissemination for the SSDs which are difficult to get code through mobile vehicles. Thus, the average delay required for SSDs to get code can be reduced. (ii) k Smart storage devices are selected as interim code stations and the strategy of selecting interim code stations’ location and number for code dissemination is proposed to disseminate code efficiently. This makes mobile vehicles can get code from k + 1 code station for code dissemination which changed the situation where code dissemination is slow due to only one code station disseminates code. (iii) This study also gives the optimisation strategy of the UAV flight trajectory to optimise the flight distance. The key performance indicators such as code dissemination rate compared to previous strategies increases 34.6%, the average dissemination time, the time that all SSDs get the code and the UAV flight trajectory's path decrease by 26.27, 25.30, and 71.67%, respectively.

Optimizing flight trajectory of UAV for efficient data collection in wireless sensor networks

Unmanned Aerial Vehicles (UAVs) are expected to be important components in the upcoming wireless communication field, which are increasingly used as data collectors to gather sensory data from Wireless Sensor Networks (WSNs) due to their high mobility, flexible deployment. Since the storage capacity and lifetime of sensors are increasing with the development of science and technology, sensors can store more and more sensing data about the monitoring area. However, due to the energy limitation of UAVs and a large amount of data carried by sensors, we can not collect all data from WSN within the limited time. Therefore, in this paper, we investigate two problems: (1) without the energy limitation of UAV, how to optimize the trajectory of UAV to minimize the sum of traveling time and data transmission time of UAV while guaranteeing the amount of data collected from each sensor reaches to a certain proportion of the original data, which is called the Minimizing Transportation and Communication Latency (MTCL) problem; (2) given the limited budget of UAV, how to find the optimal trajectory of UAV to maximize the minimum ratio of the collected data to the stored data among all sensors, which is called the Maximizing Data Collection Proportion (MDCP) problem. We first prove that both the problems are NP-Complete. Then we study a special case of the MTCL problem, which is called the MTCL-disjoint problem, in which any pair of data collection areas are disjoint, and we propose an approximation algorithm to solve the MTCL-disjoint problem. Based on the MTCL-disjoint problem, we propose an approximation algorithm for the general MTCL problem. Afterward, an approximation algorithm for the MDCP problem is proposed on the basis of the algorithm for the MTCL problem. Finally, we present numerical results in different scenarios to assess the effectiveness of the proposed algorithms.

UAV Mission Planning with SAR Application.

The paper presents the concept of mission planning for a short-range tactical class Unmanned Aerial Vehicle (UAV) that recognizes targets using the sensors it has been equipped with. Tasks carried out by such systems are mainly associated with aerial reconnaissance employing Electro Optical (EO)/Near Infra-Red (NIR) heads, Synthetic Aperture Radar (SAR), and Electronic Intelligence (ELINT) systems. UAVs of this class are most often used in NATO armies to support artillery actions, etc. The key task, carried out during their activities, is to plan a reconnaissance mission in which the flight route will be determined that optimally uses the sensors’ capabilities. The paper describes the scenario of determining the mission plan and, in particular, the UAV flight routes to which the recognition targets are assigned. The problem was decomposed into several subproblems: assigning reconnaissance tasks to UAVs with choosing the reconnaissance sensors and designating an initial UAV flight plan. The last step is planning a detailed flight route taking into account the time constraints imposed on recognition and the characteristics of the reconnaissance sensors. The final step is to generate the real UAV flight trajectory based on its technical parameters. The algorithm for determining exact flight routes for the indicated reconnaissance purposes was also discussed, taking into account the presence of enemy troops and available air corridors. The task scheduling algorithm—Vehicle Route Planning with Time Window (VRPTW)—using time windows is formulated in the form of the Mixed Integer Linear Problem (MILP). The MILP formulation was used to solve the UAV flight route planning task. The algorithm can be used both when planning individual UAV missions and UAV groups cooperating together. The approach presented is a practical way of establishing mission plans implemented in real unmanned systems.

Development of the unmanned aerial vehicle safe flight trajectorycalculation and adjustment process immitation model

The article is devoted to the development and research the unmanned aerial vehicle safe flight trajectory calculation and adjustment process imitation model. The main distinguishing feature of the presented model is taking into account the signals of radar stations during the determination and adjustment the location of the aerial vehicle in the case of an autonomous flight and adapting the route based on possible obstacles and deviations. This will improve the safety of the unmanned aerial vehicle flight trajectory. Main studies of path planning methods approaches are analyzed. It was concluded that two-dimensional representation and visualization has disadvantages. Based on disadvantages, the three-dimensional calculated trajectory visualization of a flight mission by an unmanned aerial vehicle simulation model is developed. The simulation model combines the solution of following subtasks: a) modeling a three-dimensional environment with obstacles; b) building an unmanned aerial vehicle trajectory in such environment with avoidance the obstacles. Missions simulation is performed based on developed software system, sequentially, step by step. The following results of the simulation are achieved: 1) a special graphical interface for entering input data and displaying the results is developed; 2) the subsystem for simulation the space for performing a flight task taking into account the location and signals of external influences (for example, radar stations) was developed; 3) the subsystem for flight trajectory simulation calculation and visualization is developed. The software system is built using Delphi based on modular basis. The functional software system structure and the order of each functional module operation are described in article. The system’s graphical interface structure is discussed separately. An example of calculating and adjusting the safe flight trajectory of an unmanned aerial vehicle is illustrated.

Average Peak Age-of-Information Minimization in UAV-Assisted IoT Networks

Motivated by the need to ensure timely delivery of information (e.g., status updates) in the Internet-of-things (IoT) paradigm, this paper investigates the role of an Unmanned aerial vehicle (UAV) as a mobile relay to minimize the average Peak Age-of-information (PAoI) for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. In order to solve this non-convex problem, we propose an efficient iterative algorithm and establish its convergence analytically. Closed-form solutions for some sub-problems are also provided. One of the sub-problems we solve in this procedure is to jointly optimize the energy and service time allocations for a given trajectory of the UAV. This problem is of interest on its own right because in some cases we may not be able to alter the UAV's trajectory based on the locations of the IoT devices (especially when its primary mission is something else). Our numerical results quantify the gains that can be achieved by additionally optimizing the UAV's trajectory.