Some Aspects of Software for Coordinate Determination during Autonomous UAV Flight

The modern development of unmanned aerial vehicles is extremely important for the defense capability, sovereignty and economy of Ukraine. The range of practical applications of unmanned aerial vehicles is very wide. The most important tasks of unmanned aerial vehicles are related to their use in the military, civilian (public, private, commercial) and anti-terrorist industries. Unmanned aerial vehicles have a number of advantages: high maneuverability, widely used in all areas of human activity, reliability and economy. Unmanned aerial vehicles are used in a wide range of applications, such as research, civil engineering, military use, aerial photography, search and rescue operations, and risk zone surveys. One of the most important classes of unmanned aerial vehicles are quadcopters, which have significant advantages in many parameters, such as simplicity of design, rapid manufacture and low cost. In recent years, the subject of many scientific studies in the field of quadcopters has been the control of their position, in part the height of the quadcopter. During these scientific studies, many algorithms were proposed to solve the problem of quadcopter control. The article presents a system of automatic control of unmanned aerial vehicles based on Kalman filter with improved characteristics, which allows to increase the efficiency of control and measurement of coordinates of unmanned aerial vehicles. The results of mathematical modeling of controllers used in the system of automatic control of unmanned aerial vehicles based on the Kalman filter are presented. The structural scheme of the unmanned aerial vehicle automatic control system based on the Kalman filter with an optimized measurement system that uses current state generation, reference angle generation and control signals is studied, and the assessment of horizontal and vertical positioning is performed by separate programs according to specified control algorithms. A comparative analysis of the PID controller and LQ controller during the test flight, taking into account the actual and specified altitude.

This paper presents a robust kinematic control of unmanned UAV aerial vehicles with non-holonomic constraints. The studied system consists of a 2D UAV non-holonomic kinematic model represented as a driftless system with its state and control inputs. The first part of this study consists of the evidence that the model being studied is non-holonomic in view of its involutivity properties. The second part of this study consists of the design of a robust kinematic controller for an unmanned aerial vehicle (UAV) in which the states of the systems are used for feedback control and the desired angular and linear velocities are precisely tracked by the proposed controller approach. This control strategy is achieved by designing the appropriate Lyapunov functional to meet the robust stability conditions and by finding the switching gains to track the desired profile. The control strategy obtained is tested in the proposed 2D mathematical model of the unmanned aerial vehicle and it is confirmed that the system variables track the desired profile while keeping the angular and linear velocity bounded. This study concludes with a discussion of the results and the respective conclusions.

In this paper, we will introduce our newly developed 3D simulation system for miniature unmanned aerial vehicles (UAVs) navigation and control in GPS-denied environments. As we know, simulation technologies can verify the algorithms and identify potential problems before the actual flight test and to make the physical implementation smoothly and successfully. To enhance the capability of state-of-the-art of research-oriented UAV simulation system, we develop a 3D simulator based on robot operation system (ROS) and a game engine, Unity3D. Unity3D has powerful graphics and can support high-fidelity 3D environments and sensor modeling which is important when we simulate sensing technologies in cluttered and harsh environments. On the other hand, ROS can provide clear software structure and simultaneous operation between hardware devices for actual UAVs. By developing data transmitting interface and necessary sensor modeling techniques, we have successfully glued ROS and Unity together. The integrated simulator can handle real-time multi-UAV navigation and control algorithms, including online processing of a large number of sensor data.

: Unmanned aerial vehicles (UAVs) are critical components of the future naval forces. UAV control and monitoring with autonomous operation will become an absolute necessity and adaptive cooperation of vehicles is the only practical alternative. The objective of this project is to develop and evaluate new methodologies for cooperative (formation) control of multiple unmanned air vehicles. The goal is to have multiple UAVs working together as a group. Instead of separately assigning distinct tasks to each vehicle, the operator would assign tasks to the UAV group, which then determines the best way to accomplish each task, freeing the operator to maintain surveillance over the entire operation. In this project we investigated Path Tracking and obstacle avoidance of UAVs using fuzzy logic method. Algorithms for close formation control of multi-UAVs are developed and simulated. We also investigated fault-tolerant control of single UAVs by neuro-adaptive method. Detailed description of this method is provided in this document. The project has supported S graduate students with 9 technical papers published.

This research explores the attitude control of Unmanned Aerial Vehicles (UAVs) using fuzzy control theory. As UAV applications expand in military, agriculture, logistics, and surveying, traditional control methods like Proportional-Integral-Derivative (PID) often struggle with dynamic conditions, leading to reduced stability and mission success. Fuzzy control offers adaptability and the ability to handle nonlinearities, addressing uncertainties in UAV flight. By integrating fuzzy control, we achieve rapid and precise adjustments to UAV attitude, enhancing stability and response in complex environments. This study simplifies the control model, improves adaptability, and ensures flight safety through adaptive fuzzy rules. The findings provide new insights and practical solutions for advancing UAV flight control technology, establishing a foundation for broader UAV applications.

This paper treats the question of formationflight control of multiple unmanned aerial vehicles (UAVs). Inclose formation the wing UAV motion is affected by the vortexof the adjacent lead aircraft. The forces produced by these vorticesare complex functions of the relative position coordinates ofthe UAVs. In this paper, these forces are treated as unknownfunctions. For simplicity, it is assumed that the UAVs have autopilotsfor heading-, altitude-, and Mach-hold in the inner loops. Anadaptive control law is derived for the position control of thewing aircraft based on a backstepping design technique. In theclosed-loop system, commanded separation trajectories are asymptoticallytracked by each wing aircraft while the lead UAV is maneuvering.It is seen that an overparametrization in the design is essentialfor the decentralization of the control system. These resultsare applied to formation flight control of two UAVs and simulationresults are obtained. These results show that the wing UAV followsprecisely the reference separation trajectories in spite of theuncertainties in the aerodynamic coefficients, while the leadaircraft maneuvers.

We propose a lightweight embedded system for stabilization and control of Unmanned Aerial Vehicles (UAVs) and particularly Micro Aerial Vehicles (MAVs). The system relies solely on onboard sensors to localize the MAV, which makes it suitable for experiments in GPS-denied environments. The system utilizes predictive controllers to find optimal control actions for the aircraft using only onboard computational resources. To show the practicality of the proposed system, we present several indoor and outdoor experiments with multiple quadrotor helicopters which demonstrate its capability of trajectory tracking and disturbance rejection.

Most of the recent research on distributed formation control of unmanned aerial vehicle (UAV) swarms is founded on position, distance, and displacement-based approaches; however, a very promising approach, i.e., bearing-based formation control, is still in its infancy and needs extensive research effort. In formation control problems of UAVs, Euler angles are mostly used for orientation calculation, but Euler angles are susceptible to singularities, limiting their use in practical applications. This paper proposed an effective method for time-varying velocity and orientation leader agents for distributed bearing-based formation control of quadcopter UAVs in three-dimensional space. It combines bearing-based formation control and quaternion-based attitude control using undirected graph topology between agents without the knowledge of global position and orientation. The performance validation of the control scheme was done with numerical simulations, which depicted that UAV formation achieved the desired geometric pattern, translation, scaling, and rotation in 3D space dynamically.

For the control of unmanned aerial vehicles (UAVs) in GPS-denied environments, cameras have been widely exploited as the main sensory modality for addressing the UAV state estimation problem. However, the use of visual information for ego-motion estimation presents several theoretical and practical difficulties, such as data association, occlusions, and lack of direct metric information when exploiting monocular cameras. In this paper, we address these issues by considering a quadrotor UAV equipped with an onboard monocular camera and an inertial measurement unit (IMU). First, we propose a robust ego-motion estimation algorithm for recovering the UAV scaled linear velocity and angular velocity from optical flow by exploiting the so-called continuous homography constraint in the presence of planar scenes. Then, we address the problem of retrieving the (unknown) metric scale by fusing the visual information with measurements from the onboard IMU. To this end, two different estimation strategies are proposed and critically compared: a first exploiting the classical extended Kalman filter (EKF) formulation, and a second one based on a novel nonlinear estimation framework. The main advantage of the latter scheme lies in the possibility of imposing a desired transient response to the estimation error when the camera moves with a constant acceleration norm with respect to the observed plane. We indeed show that, when compared against the EKF on the same trajectory and sensory data, the nonlinear scheme yields considerably superior performance in terms of convergence rate and predictability of the estimation. The paper is then concluded by an extensive experimental validation, including an onboard closed-loop control of a real quadrotor UAV meant to demonstrate the robustness of our approach in real-world conditions.

Purpose Autonomous flight of unmanned aerial vehicles (UAVs) in global position system (GPS)-denied environments has become an increasing research hotspot. This paper aims to realize the indoor fixed-point hovering control and autonomous flight for UAVs based on visual inertial simultaneous localization and mapping (SLAM) and sensor fusion algorithm based on extended Kalman filter. Design/methodology/approach The fundamental of the proposed method is using visual inertial SLAM to estimate the position information of the UAV and position-speed double-loop controller to control the UAV. The motion and observation models of the UAV and the fusion algorithm are given. Finally, experiments are performed to test the proposed algorithms. Findings A position-speed double-loop controller is proposed, by fusing the position information obtained by visual inertial SLAM with the data of airborne sensors. The experiment results of the indoor fixed-points hovering show that UAV flight control can be realized based on visual inertial SLAM in the absence of GPS. Originality/value A position-speed double-loop controller for UAV is designed and tested, which provides a more stable position estimation and enabled UAV to fly autonomously and hover in GPS-denied environment.

At present, more and more applications of Unmanned Aerial Vehicles (UAV) makes the control of UAV to be a hot pot in doing research. In the controlling of UAV, traditional PID controller's parameters are not easily chose and anti-interference ability of the control system is poor. Aiming to improve it, the paper designs a GPFN-PID controller based on GPFN neural network. The controller has the functions of online training, self-training and self-adjusting, which makes the control of UAV longitudinal channel more effective. The simulation results show that both the dynamic characteristics and the anti-interference ability of the control system improve a lot.

The stability control of unmanned aerial vehicle (UAV) is the most important technology when it is on the duty of taking area photos. This paper aims at analyzing the structures, the corresponding channel of control and control modes in small fixed-wing UAV, and focuses on flight control system, which systematically reveals the running rules of UAV. According to the control theories in UVA, the paper explains the method of stability controlling UAV from two major aspects, the longitudinal and lateral movement control, based on the UAV flight operation process. By understanding the structures, the modes, the rules and the theories, the paper achieves the goal that can catch the method of fully controlling UAV.

This paper presents the different stages of development of flight controller for unmanned aerial vehicle (UAV) with autonomously flying capability. The main purpose of flight controller is to perform a petrol function between given GPS coordinates while maintaining a specified altitude for an assigned time and then return to its launching position without any human interaction. At first a simulated environment was designed to verify the concept. Then this was implemented on actual unmanned helicopter. In wake of the current political and security situation the need for autonomous UAVs and particularly the small scale helicopters are under consideration. However, hobby airframes used for normal radio controlled flying are not readily compatible with delicate, sophisticated and complex electronics. In addition to the hardware integration with such airframes, several other challenges such as electromagnetic interference, communication signal integrity from jammers and damping of strong vibrations were handled. The goal is to develop an autonomous UAV with self-controlled flying capabilities and above mentioned functionality. It should also fulfil the requirements to perform an adequate surveillance during any political and security situation.

Today, the use of small unmanned aerial vehicles (UAVs) has increased due to technological advances. There has been an interest in using multiple UAVs instead of a single UAV to accomplish a given mission. This is because there are many scenarios where the capabilities of a single UAV are inadequate due to certain constraints (battery capacity, time in the air). For this reason, swarm UAV studies have increased. A swarm UAV consists of a large number of UAVs cooperating to accomplish a specific mission. This study shows three-dimensional formation control of a swarm UAV system in an obstacle environment. A centralized control architecture is used in this process. All task assignments are made from a centralized system. The Artificial potential fields method creates the formation at the target point by avoiding obstacles. The study was carried out using the Robot Operating System (ROS). The methods were tested in Webots simulation environment. Crazyflie robots were used in the experiments. In the simulation environment, square, star and v formations were first tested in two dimensions. Then, as an example of three-dimensional formation, the cubic and pyramid formations were created and observed.

In this paper we discuss the design issues and the corresponding systematic sequence of events related to a basic controller for an Unmanned Aerial Vehicle (UAV). To this end, we first present the details of a basic PID controller, We then describe the equations and investigate the problems of how these equations will be approximated for determining the dynamic longitudinal movement of UAV. After specifying the model of the system and clarifying the details of the designed algorithms, we will identify the gains corresponding to the coefficients of the PID controller using the well-known AeroSim simulator software. The computed gains and the results drawn from the simulation process will be used to analyze the UAV stability against important metrics such as air speed and pitch angle.