Problem Of Unmanned Aerial Vehicles Research Articles

Adaptive finite-time reconfiguration control of unmanned aerial vehicles with a moving leader

This paper investigates adaptive reconfiguration control problem for unmanned aerial vehicle (UAV) helicopter system with a moving leader. Only part of UAV helicopter is informed to have access to the leader’s position. The six degree-of-freedom UAV system is composed of position outer loop and attitude inner loop. In this paper, we introduce a new fully distributed, finite-time reconfiguration controller and the problem of inter-UAVs collision avoidance was solved using potential energy function approach, extending the asymptotical formation controller without collision avoidance from the literature. The distinctive feature of our algorithm from existing works is that the novel formation reconfiguration controller can achieve finite-time, collision avoidance and fully distributed formation only based on relative positions between UAV and its adjacents. It means that the control algorithm is independent of any global information that requires to be calculated by each follower UAV. The system uncertainties are estimated by radial basis function neural network in practical finite time. Simulation results are shown to demonstrate the efficiency of the designed strategy.

Distributed Cooperative Onboard Planning for the Conflict Resolution of Unmanned Aerial Vehicles

The distributed cooperative onboard conflict resolution problem of unmanned aerial vehicles is studied in this paper. First, the dynamics of unmanned aerial vehicles and the safe separation constraint are studied. The monotonicity feature of the constraint condition is analyzed. The distributed conflict resolution problem is modeled as a distributed constraint optimization problem. Second, the distributed constraint optimization problem is solved in two steps. The feasible solutions are searched first, and the overall costs are minimized thereafter. The distributed stochastic gradient descent method is proposed to search the local near-optimal solutions. The self-improving mechanism is proposed to enhance the utilization efficiency of data exchanged among unmanned aerial vehicles. The “virtue adjustment” theorem is defined to motivate the cooperative coordination between unmanned aerial vehicles. Simulation results show that, compared with existing short-term algorithms, our algorithm could reduce the summation of additional flight distances and impacts on flight plans of unmanned aerial vehicles significantly and coordinate the flights of unmanned aerial vehicles in complex scenarios.

Unmanned Aerial Vehicle-Aided Communications: Joint Transmit Power and Trajectory Optimization

This letter investigates the transmit power and trajectory optimization problem for unmanned aerial vehicle (UAV)-aided networks. Different from majority of the existing studies with fixed communication infrastructure, a dynamic scenario is considered where a flying UAV provides wireless services for multiple ground nodes simultaneously. To fully exploit the controllable channel variations provided by the UAV’s mobility, the UAV’s transmit power and trajectory are jointly optimized to maximize the minimum average throughput within a given time length. For the formulated non-convex optimization with power budget and trajectory constraints, this letter presents an efficient joint transmit power and trajectory optimization algorithm. Simulation results validate the effectiveness of the proposed algorithm and reveal that the optimized transmit power shows a water-filling characteristic in spatial domain.

UAV Broken-Line Path Following under Disturbance Conditions

AbstractFor the broken-line path following problem of unmanned aerial vehicles (UAVs), to enhance the ability to reject wind disturbance and reduce position error overshoot when a UAV turns in the ...

Joint Altitude, Beamwidth, Location, and Bandwidth Optimization for UAV-Enabled Communications

This letter investigates an uplink power control problem for unmanned aerial vehicles (UAVs)-assisted wireless communications. We jointly optimize the UAV's flying altitude, antenna beamwidth, UAV's location, and ground terminals' allocated bandwidth, and transmit power to minimize the sum uplink power subject to the minimal rate demand. An iterative algorithm is proposed with low complexity to obtain a suboptimal solution. Numerical results show that the proposed algorithm can achieve good performance in terms of uplink sum power saving.

Joint routing and aborting optimization of cooperative unmanned aerial vehicles

This paper incorporates the abort policy into the routing problem of unmanned aerial vehicles (UAV). In order to serve a number of targets, some UAVs can be deployed each visiting part of the targets. Different from other works on routing of UAVs, it is assumed that each UAV may experience shocks during the travel. In order to reduce the expected cost of UAV destruction, it is allowed that a UAV aborts the mission if it is found to have undergone too many shocks after it finishes serving a certain number of targets. The optimal routing plan together with the abort policy for each UAV are studied, with the objective to minimize the total cost consisting of the expected cost of UAV destruction and the expected cost for unvisited targets. Test case is used to illustrate the application of the framework.

An active disturbance rejection control guidance law based collision avoidance for unmanned aerial vehicles

In this paper, an active disturbance rejection control guidance law is proposed for the problem of unmanned aerial vehicle (UAV) collision avoidance. First, a linear time-varying collision avoidance model based on a collision cone is established. Then the active disturbance rejection control guidance law is designed to ensure the security of UAV collision avoidance. In addition, the stability of a nonlinear active disturbance rejection system with an extended state observer is proved by the circle criterion, and the stability conditions are used to design the guidance coefficients. A simulation system based on a six-degrees-of-freedom UAV model is used to demonstrate the performance of this guidance law. The results conclusively demonstrate that this method can achieve collision avoidance in the presence of sensor noise, an unknown acceleration of an obstacle, and wind disturbance. Moreover, the manoeuvring range of collision avoidance using this method is narrower than the collision avoidance method based on nonlinear dynamic inversion guidance.

Observer design for monocular visual inertial SLAM

Abstract This paper examines the state estimation problem for unmanned aerial vehicles when commonly used positioning systems such as the global positioning system or indoor motion capture systems are unavailable. The proposed method uses inertial sensor measurements along with scaled position measurements from an onboard computer vision system which implements visual simultaneous localization and mapping. A state transformation puts the system into a linear time-varying form which simplifies observability analysis and allows for an observer design with sufficient conditions for convergence. The proposed design is validated by simulation.

Multilabel Conditional Random Field Classification for UAV Images

In this letter, we formulate the multilabeling classification problem of unmanned aerial vehicle (UAV) imagery within a conditional random field (CRF) framework with the aim of exploiting simultaneously spatial contextual information and cross-correlation between labels. The pipeline of the framework consists of two main phases. First, the considered input UAV image is subdivided into a grid of tiles, which are processed thanks to an opportune representation and a multilayer perceptron classifier providing thus tile-wise multilabel prediction probabilities. In the second phase, a multilabel CRF model is applied to integrate spatial correlation between adjacent tiles and the correlation between labels within the same tile, with the objective to improve iteratively the multilabel classification map associated with the considered input UAV image. Experimental results achieved on two different UAV image data sets are reported and discussed.

Resource Allocation for Energy Harvesting-Powered D2D Communication Underlaying UAV-Assisted Networks

In this paper, we investigate the resource allocation problem for unmanned aerial vehicle (UAV)-assisted networks, where a UAV acting as an energy source provides radio frequency energy for multiple energy harvesting-powered device-to-device (D2D) pairs with much information to be transmitted. The goal is to maximize the average throughput within a time horizon while satisfying the energy causality constraint under a generalized harvest-transmit-store model, which results in a non-convex problem. By introducing the Lagrangian relaxation method, we analytically show that the behavior of all D2D pairs at each time slot is exclusive: harvesting energy or transmitting information signals. The formulated non-convex optimization problem is thus transformed into a mixed integer nonlinear programming (MINIP). We then design an efficient resource allocation algorithm to solve this MINIP, where D.C. (difference of two convex functions) programming and golden section method are combined to achieve a suboptimal solution. Furthermore, we provide an idea to reduce the computational complexity for facilitating the application in practice. Simulations are conducted to validate the effectiveness of the proposed algorithm and evaluate the system throughput performance.

Virtual Target based Obstacle Avoiding Guidance

The problem of Unmanned Aerial Vehicle(UAV) obstacle avoidance is considered. The UAV follows a virtual target of varying velocity with pure pursuit guidance such that the separation between them remains constant. The analysis of the guidance law is carried out and closed form expressions for virtual target’s deviation angle is obtained. The analytic results are interpreted geometrically. Simulation studies demonstrate the effectiveness of the guidance law for different obstacle sizes.

A data authentication scheme for UAV ad hoc network communication

In this paper, we propose an efficient and energy-saving distributed network architecture based on clustering stratification to solve the information security problem of unmanned aerial vehicle ad hoc network communication. And a double-authentication watermarking strategy is designed. In order to ensure that the data collected by nodes can be sent securely to the cluster head node, we use the self-characteristic of the collected data to generate the authentication watermark and insert it into the collected data at random. The cluster head node first verifies the integrity of collected data and deletes the suspicious data. Then, the authentication information is generated by combining the chaotic mapping method, and the watermark is hidden by changing the parity of the least significant bits of the data. Experimental results show that the proposed security strategy can resist most attacks, such as selective forwarding, data replay and tampering. Meanwhile, it has low energy consumption and low latency.

Autonomous Navigation for an Unmanned Aerial Vehicle by the Decomposition Coordination Method

This paper introduces a new approach for solving the navigation problem of Unmanned Aerial Vehicles (UAV) by studying its rotational and translational dynamics and then solving the nonlinear model by the Decomposition Coordination method. The objective is to reach a destination goal by the mean of an autonomous computed optimal path calculated through optimal control sequence. Solving such complex systems often requires a great amount of computation. However, the approach considered herein is based on the Decomposition Coordination principle, which allows the nonlinearity to be treated at a local level, thus offering a low computing time. The stability of the method is discussed with sufficient conditions for convergence. A numerical application is given in consolidation the theoretical results.

A linear time algorithm for the [formula omitted]-neighbour Travelling Salesman Problem on a Halin graph and extensions

The Quadratic Travelling Salesman Problem (QTSP) is to find a least cost Hamiltonian cycle in an edge-weighted graph, where costs are defined for all pairs of edges contained in the Hamiltonian cycle. The problem is shown to be strongly NP-hard on a Halin graph. We also consider a variation of the QTSP, called the k-neighbour TSP (TSP(k)). Two edges e and f, e≠f, are k-neighbours on a tour τ if and only if a shortest path (with respect to the number of edges) between e and f along τ and containing both e and f, has exactly k edges, for k≥2. In (TSP(k)), a fixed nonzero cost is considered for a pair of distinct edges in the cost of a tour τ only when the edges are p-neighbours on τ for 2≤p≤k. We give a linear time algorithm to solve TSP(k) on a Halin graph for k=3, extending existing algorithms for the cases k=1,2. Our algorithm can be extended further to solve TSP(k) in polynomial time on a Halin graph with n nodes when k=O(logn). The possibility of extending our results to some fully reducible class of graphs is also discussed. TSP(k) can be used to model the Permuted Variable Length Markov Model in bioinformatics as well as an optimal routing problem for unmanned aerial vehicles (UAVs).

Modeling and simulation of dynamic ant colony’s labor division for task allocation of UAV swarm

The problem of unmanned aerial vehicle (UAV) task allocation not only has the intrinsic attribute of complexity, such as highly nonlinear, dynamic, highly adversarial and multi-modal, but also has a better practicability in various multi-agent systems, which makes it more and more attractive recently. In this paper, based on the classic fixed response threshold model (FRTM), under the idea of “problem centered + evolutionary solution” and by a bottom-up way, the new dynamic environmental stimulus, response threshold and transition probability are designed, and a dynamic ant colony’s labor division (DACLD) model is proposed. DACLD allows a swarm of agents with a relatively low-level of intelligence to perform complex tasks, and has the characteristic of distributed framework, multi-tasks with execution order, multi-state, adaptive response threshold and multi-individual response. With the proposed model, numerical simulations are performed to illustrate the effectiveness of the distributed task allocation scheme in two situations of UAV swarm combat (dynamic task allocation with a certain number of enemy targets and task re-allocation due to unexpected threats). Results show that our model can get both the heterogeneous UAVs’ real-time positions and states at the same time, and has high degree of self-organization, flexibility and real-time response to dynamic environments.

Ground Target Tracking Guidance Law for Fixed-Wing Unmanned Aerial Vehicle: A Search and Capture Approach

One important problem for unmanned aerial vehicles (UAVs) in mission applications is to track ground targets automatically. A major concern is how to keep the tracking process stable and efficient while the motion of the ground targets changes rapidly. In this brief, a new guidance strategy for the ground target “Search and Capture” based on a virtual target is proposed. First, a virtual trajectory, which is composed of straight lines and arcs, is generated based on the motion of the target. The straight lines are used to capture, while the arcs are used to search, and switch between straight line and arc when some condition is met; second, we design a new guidance law based on line-of-sight (LOS) which makes a UAV to track the virtual target automatically. This new method solves the following three problems simultaneously: (1) The UAV always keeps a constant speed to track the target with changing velocity, (2) the generated trajectory meets the flight constraints of the UAV, and (3) the speed range of the ground target can be from the stationary to almost the maximum cruising speed of the UAV. Simulation results show that the proposed guidance strategy can achieve stable tracking for various motions of the ground target.

$\mathcal {L}_{1}$ Adaptive Backstepping for Robust Trajectory Tracking of UAVs

This paper addresses the trajectory tracking control (i.e., outer-loop control) problem for unmanned aerial vehicles (UAVs) in the presence of modeling uncertainties and external disturbances. The kinematics and dynamics of the trajectory tracking problem are always in strict feedback form. While there is no uncertainty in the kinematics, rapidly changing uncertainties present in the dynamics makes this problem an ideal candidate for $\mathcal {L}_{1}$ adaptive backstepping control. The trajectory tracking controller serves as the outer-loop in a cascaded design architecture and supplies a reference quaternion and thrust to the inner-loop controller. The inner-loop controller in turn generates a moment demand for the control allocation module. Such an inner-outer loop architecture is modular and does away with the requirement that the commanded trajectory be four times differentiable as would be required if a single monolithic backstepping control law where to be used. Both simulations and flight tests are used to demonstrate the effectiveness of the proposed controller and how the controller tracks any twice differentiable trajectory, respectively.

Моделювання прийняття рішень оператором дистанційно пілотованого літального апарату в аварійних ситуаціях

Мета: Відсутність алгоритму рекомендацій дій оператора БПЛА в надзвичайних ситуаціях, розкладання процесу прийняття рішень оператором БПЛА в надзвичайних ситуаціях, розробка структури системи підтримки прийняття рішень для оператора БПЛА, розробка моделей прийняття рішень оператором БПЛА. Методи: Створення алгоритму прийняття рішень за допомогою критеріїв Вальда, Гурвіца, Ла Пласа. Результати: програма "UAV_AS", що дає рекомендації оператору БПЛА про те, як діяти в разі виникнення надзвичайної ситуації. Авторами розроблено алгоритм дій рекомендації оператора БПЛА в надзвичайних ситуаціях. Представлений процес прийняття рішень оператором БПЛА в надзвичайних ситуаціях у вигляді детермінованої моделі із застосуванням методів мережевого моделювання. Побудовано мережевий граф, за яким визначено критичний час на парирування надзвичайної ситуації у разі польоту БПЛА. Розроблено структуру розподіленої СППР для колективного управління пілотованих та безпілотних літальних апаратів оператором. Авторами розроблено бази даних для локальних СППР операторів управління БПЛА, Розроблено моделі прийняття рішень оператором БПЛА в умовах ризику та в умовах невизначеності у разі аварійної посадки БПЛА. Обговорення: У статті розглядаються проблеми польотів безпілотних літальних апаратів для вирішення різних завдань в надзвичайній ситуації, засноване на статистичних даних від аналізу типів надзвичайних ситуацій для безпілотних літальних апаратів. Визначається послідовність дій оператора БПЛА в разі виникнення надзвичайних ситуацій.

Heuristic and Genetic Algorithm Approaches for UAV Path Planning under Critical Situation

The present paper applies a heuristic and genetic algorithms approaches to the path planning problem for Unmanned Aerial Vehicles (UAVs), during an emergency landing, without putting at risk people and properties. The path re-planning can be caused by critical situations such as equipment failures or extreme environmental events, which lead the current UAV mission to be aborted by executing an emergency landing. This path planning problem is introduced through a mathematical formulation, where all problem constraints are properly described. Planner algorithms must define a new path to land the UAV following problem constraints. Three path planning approaches are introduced: greedy heuristic, genetic algorithm and multi-population genetic algorithm. The greedy heuristic aims at quickly find feasible paths, while the genetic algorithms are able to return better quality solutions within a reasonable computational time. These methods are evaluated over a large set of scenarios with different levels of diffculty. Simulations are also conducted by using FlightGear simulator, where the UAV’s behaviour is evaluated for different wind velocities and wind directions. Statistical analysis reveal that combining the greedy heuristic with the genetic algorithms is a good strategy for this problem.

Dynamic path planning based on improved boundary value problem for unmanned aerial vehicle

When unmanned aerial vehicles (UAVs) track a moving target in complex environment, most path planning methods are difficult to combine real-time calculation and optimality. A real-time path planning method based on boundary value problem (BVP) is studied. First, a grid model of terrain is established based on UAV constraints. Then the structure of receding horizon planning ensures the real-time tracking to moving target, and the path is determined based on BVP in each horizon window. The sub-goal of horizon window is designed by line-of-sight, and the method of updating potential field dynamically and the calculation of flight direction are proposed. By comparison with other methods, the simulation results show that the method can ensure the path smooth and feasible. It can track the moving target in real-time and have some optimality, which is suitable for the mission of UAV to track moving target in complex environment.