Fixed-wing Unmanned Air Vehicles Research Articles

Long-Distance GNSS-Denied Visual Inertial Navigation for Autonomous Fixed-Wing Unmanned Air Vehicles: SO(3) Manifold Filter Based on Virtual Vision Sensor

This article proposes a visual inertial navigation algorithm intended to diminish the horizontal position drift experienced by autonomous fixed-wing UAVs (unmanned air vehicles) in the absence of GNSS (Global Navigation Satellite System) signals. In addition to accelerometers, gyroscopes, and magnetometers, the proposed navigation filter relies on the accurate incremental displacement outputs generated by a VO (visual odometry) system, denoted here as a virtual vision sensor, or VVS, which relies on images of the Earth surface taken by an onboard camera and is itself assisted by filter inertial estimations. Although not a full replacement for a GNSS receiver since its position observations are relative instead of absolute, the proposed system enables major reductions in the GNSS-denied attitude and position estimation errors. The filter is implemented in the manifold of rigid body rotations or SO(3) in order to minimize the accumulation of errors in the absence of absolute observations. Stochastic high-fidelity simulations of two representative scenarios involving the loss of GNSS signals are employed to evaluate the results. The authors release the C++ implementation of both the visual inertial navigation filter and the high-fidelity simulation as open-source software.

Design and Development of Flapping Wing UAV

Flapping wing air vehicles are unmanned miniature aircraft’s that draw inspiration from birds, bats and insects. They utilize continuous flapping wing motion to generate aerodynamic forces and moments. The fluid structure interaction analysis of biological and artificial flapping flyers is extremely complicated thus an efficient unsteady aerodynamic model is required in such time consuming problems as optimal flapping wing simulation. This project deals with designing a flapping wing air vehicle which includes applying inertial impulsive force to UAV in such a way that the flapping wing air vehicle reaches almost constant flight speed after removing the initial impulsive forces. The flow dynamics are different of the flapping wing air vehicles with that of the fixed wing UAV and this project deals with the utilization of the flapping wing in the UAV to overcome the challenges faced by a fixed wing UAV like maneuverability in remote areas, sound produced and identification during mission. Structural and aerodynamic characteristics of a bird in flight offer benefits over typical propeller or rotor driven unmanned air vehicle (UAV). Hence this study deals with the designing of an flapping wing UAV to overcome these drawbacks of fixed wing UAV.

2-D path following for fixed wing UAV using global fast terminal sliding mode control

This paper considers the finite time convergence problem of 2-D path following for fixed wing unmanned air vehicle. Firstly, the UAV path following model is divided into an outer guidance loop and an inner control loop. Then, the guidance loop and control loop controllers of the UAV are derived by global fast terminal sliding mode control technique, which is able to guarantee the system state variables converge to expected values in finite time and eliminate the chattering phenomenon caused by the switching control action. Furthermore, the stability of the two-loops system is proven by Lyapunov stability theory. Finally, the simulation results are shown to verify the performance of the proposed method.

Propeller Position Effects over the Pressure and Friction Coefficients over the Wing of an UAV with Distributed Electric Propulsion: A Proper Orthogonal Decomposition Analysis

New propulsive architectures, with high interactions with the aerodynamic performance of the platform, are an attractive option for reducing the power consumption, increasing the resilience, reducing the noise and improving the handling of fixed-wing unmanned air vehicles. Distributed electric propulsion with boundary layer ingestion over the wing introduces extra complexity to the design of these systems, and extensive simulation and experimental campaigns are needed to fully understand the flow behaviour around the aircraft. This work studies the effect of different combinations of propeller positions and angles of attack over the pressure coefficient and skin friction coefficient distributions over the wing of a 25 kg fixed-wing remotely piloted aircraft. To get more information about the main trends, a proper orthogonal decomposition of the coefficient distributions is performed, which may be even used to interpolate the results to non-simulated combinations, giving more information than an interpolation of the main aerodynamic coefficients such as the lift, drag or pitching moment coefficients.

Gesture commands for controlling high-level UAV behavior

Directing groups of unmanned air vehicles (UAVs) is a task that typically requires the full attention of several operators. This can be prohibitive in situations where an operator must pay attention to their surroundings. In this paper we present a gesture device that assists operators in commanding UAVs in focus-constrained environments. The operator influences the UAVs’ behavior by using intuitive hand gesture movements. Gestures are captured using an accelerometer and gyroscope and then classified using a logistic regression model. Ten gestures were chosen to provide behaviors for a group of fixed-wing UAVs. These behaviors specified various searching, following, and tracking patterns that could be used in a dynamic environment. A novel variant of the Monte Carlo Tree Search algorithm was developed to autonomously plan the paths of the cooperating UAVs. These autonomy algorithms were executed when their corresponding gesture was recognized by the gesture device. The gesture device was trained to classify the ten gestures and accurately identified them 95% of the time. Each of the behaviors associated with the gestures was tested in hardware-in-the-loop simulations and the ability to dynamically switch between them was demonstrated. The results show that the system can be used as a natural interface to assist an operator in directing a fleet of UAVs.Article highlightsA gesture device was created that enables operators to command a group of UAVs in focus-constrained environments.Each gesture triggers high-level commands that direct a UAV group to execute complex behaviors.Software simulations and hardware-in-the-loop testing shows the device is effective in directing UAV groups.

Partitioned artificial immune system for detection and identification of autonomous flight vehicle abnormal conditions

PurposeAn artificial immune system (AIS) for the detection and identification of abnormal operational conditions affecting an unmanned air vehicle (UAV) is developed using the partition of the universe approach. The performance of the proposed methodology is assessed through simulation within the West Virginia University (WVU) unmanned aerial system (UAS) simulation environment.Design/methodology/approachAn AIS is designed and generated for a fixed wing UAV using data from the WVU UAS simulator. A novel partition of the universe approach augmented with the hierarchical multiself strategy is used to define the self, within the AIS paradigm. Several 2-dimensional and 3-dimensional commanded trajectories are simulated under normal and abnormal conditions affecting actuators and sensors. Data recorded are used to build the AIS and develop an abnormal condition detection and identification scheme for the two categories of subsystems. The performance of the methodology is evaluated in terms of detection and identification rates, false alarms and decision times.FindingsThe proposed methodology for UAV abnormal condition detection and identification has the potential to support a comprehensive and integrated solution to the problem of aircraft subsystem health management. The novel partition of the universe approach has been proven to be a promising alternative to the previously investigated clustering methods by providing similar or better performance for the cases investigated.Research limitations/implicationsThe promising results obtained within this research effort motivate further investigation and extension of the proposed methodology toward a complete system health management process, including abnormal condition evaluation and accommodation.Practical implicationsThe use of the partition of the universe approach for AIS generation may potentially represent a valuable alternative to current clustering methods within the AIS paradigm. It can facilitate a simpler and faster implementation of abnormal condition detection and identification schemes.Originality/valueIn this paper, a novel method for AIS generation, the partition of the universe approach, is formulated and applied for the first time for the development of abnormal condition detection and identification schemes for UAVs. This approach is computationally less expensive and mitigates some of the issues related to the typical clustering approaches. The implementation of the proposed approach can potentially enhance the robustness of UAS for safety purposes.

An Estimator for UAV Attitude Determination based on Accelerometers, GNSS Sensors, and Aerodynamic Coefficients

Accurate navigation and control of Unmanned Air Vehicles (UAV) are strongly linked to the estimation of position and attitude. Rotation of an aircraft can be determined measuring two vectors, for example, velocity and gravity vectors, in two independent reference frameworks, for example, inertial and the body reference frames. The velocity vector can be determined in inertial reference frame from a GNSS-based sensor and in the body reference frame by integrating accelerometer measurements. A novel approach is presented for gravity vector estimation in the body reference frame, which is based on UAV aerodynamics and mass properties. Combining both vectors enables determination of body rotation. Nonlinear simulations show that, utilizing GNSS sensors, strap-down accelerometers and the proposed approach, aircraft attitude determination is accurate. The developed calculations are implemented on a fixed-wing UAV, permitting the substitution of specific attitude determination gadgets, which are normally costly for several applications. © 2018 Institute of Navigation.

Mobile Robots Enhanced by microtubular Solid Oxide Fuel Cells (mSOFCs)

Mobile robots are limited by their battery energy storage. Solid Oxide Fuel Cells running on propane or other liquid fuels can enhance the battery robot range by an order of magnitude while allowing rapid refilling. The need is to improve the system power to weight (P/W) ratio. Moving to smaller diameter microtubular SOFCs (mSOFCs) is beneficial in raising the stack P/W, but the system components including thermal insulation, cathode air heater and reformer are also important. This paper describes experiments on a 100W mSOFC system to show that 100Wkg-1 power density can be obtained by improving system components and integration. This system could power a small fixed wing Unmanned Air Vehicle (UAV) but needs to be improved by a further factor 5 to fly a helicopter.

Autolanding a Power-off UAV Using On-line Optimization and Slip Maneuvers

This paper tackles the final stages of autolanding a fixed-wing Unmanned Air Vehicle (UAV) in a power-fail emergency scenario. We applied the principle of Nonlinear Model Predictive Control (NMPC) to plan trajectories that respect the aircraft's limits, and react on disturbances and environmental changes in real-time. However, thanks to judicious problem formulations, our algorithm optimizes--in each time step--the whole horizon of the landing trajectory, and settles a dynamic compromise between the different touchdown requirements. Furthermore, the algorithm is powered by a novel method that utilizes slip maneuvers to achieve adequate energy control notwithstanding the absence of specialized drag control devices. Thus, precise touchdown point is attained even in the presence of strong wind shear and large initial errors. All design parameters are optimized off-line over a wide spectrum of conditions using Genetic Algorithm (GA). Monte-Carlo simulation of the optimized design shows excellent landing performance and high success rate on both flat and sloping terrains.

A Ground-Based Near Infrared Camera Array System for UAV Auto-Landing in GPS-Denied Environment.

This paper proposes a novel infrared camera array guidance system with capability to track and provide real time position and speed of a fixed-wing Unmanned air vehicle (UAV) during a landing process. The system mainly include three novel parts: (1) Infrared camera array and near infrared laser lamp based cooperative long range optical imaging module; (2) Large scale outdoor camera array calibration module; and (3) Laser marker detection and 3D tracking module. Extensive automatic landing experiments with fixed-wing flight demonstrate that our infrared camera array system has the unique ability to guide the UAV landing safely and accurately in real time. Moreover, the measurement and control distance of our system is more than 1000 m. The experimental results also demonstrate that our system can be used for UAV automatic accurate landing in Global Position System (GPS)-denied environments.

Quadratic Optimal Control of Aerodynamic Vectored UAV at High Angle of Attack

Small-scale Unmanned Air Vehicles (UAVs) belong to the class of less than one meter box dimension and are widely used in different civilian and military roles. In this paper, quadratic optimal control synthesis is carried out on a rigid-wing UAV at low speeds with improved aerodynamic performance. The aircraft is equipped with a unique feature of ‘aerodynamic vectoring’ such that the angle of incidence of the wing is dealt as an independent control variable. The aircraft motion is studied in longitudinal plane. More specifically, controller is designed using Linear Quadratic Regulation (LQR) and Linear Quadratic Gaussian (LQG) formulations which are carried out for two configurations – fixed-wing UAV and ‘Aerodynamic Vectored’ UAV. The control architecture shows avoidance of instabilities associated with short period and phugoid modes. Moreover, aerodynamic vectored UAV manifests better performance in terms of stability, control effort, settling time and overshoot.

Filtered dynamic inversion for altitude control of fixed-wing unmanned air vehicles

Instrumented unmanned air vehicles (UAVs) represent a new way of conducting atmospheric science, particularly within the atmospheric boundary layer where the air is turbulent. However, using autonomous UAVs for airborne measurement requires active control methods capable of following altitude commands despite unknown and turbulent disturbances to the air. Filtered dynamic inversion (FDI) is a control method with desirable command-following and disturbance-rejection properties for this application. FDI requires limited model information and is thus robust to parametric uncertainty, which arises in modeling UAV dynamics. In this paper, FDI is implemented in an altitude-flight-control system for an autonomous fixed-wing UAV. The control system is validated in simulation with a nonlinear dynamic model of a small fixed-wing UAV. The control system is also implemented and validated in flight experiments with turbulent wind conditions. Experimental results show that FDI yields improved altitude and pitch command following as compared to a classical (e.g., proportional-integral) flight-control system. In particular, experimental data demonstrate that the average power of the altitude and pitch command-following errors with FDI is smaller than those with proportional-integral control.

Multi-UAV Routing for Area Coverage and Remote Sensing with Minimum Time.

This paper presents a solution for the problem of minimum time coverage of ground areas using a group of unmanned air vehicles (UAVs) equipped with image sensors. The solution is divided into two parts: (i) the task modeling as a graph whose vertices are geographic coordinates determined in such a way that a single UAV would cover the area in minimum time; and (ii) the solution of a mixed integer linear programming problem, formulated according to the graph variables defined in the first part, to route the team of UAVs over the area. The main contribution of the proposed methodology, when compared with the traditional vehicle routing problem’s (VRP) solutions, is the fact that our method solves some practical problems only encountered during the execution of the task with actual UAVs. In this line, one of the main contributions of the paper is that the number of UAVs used to cover the area is automatically selected by solving the optimization problem. The number of UAVs is influenced by the vehicles’ maximum flight time and by the setup time, which is the time needed to prepare and launch a UAV. To illustrate the methodology, the paper presents experimental results obtained with two hand-launched, fixed-wing UAVs.

Fixed-wing approach techniques for complex environments

AbstractThe landing approach for fixed-wing small unmanned air vehicles (SUAVs) in complex environments such as urban canyons, wooded areas, or any other obscured terrain is challenging due to the limited distance available for conventional glide slope descents. Alternative approach methods, such as deep stall and spin techniques, are beneficial for such environments but are less conventional and would benefit from further qualitative and quantitative understanding to improve their implementation. Flight tests of such techniques, with a representative remotely piloted vehicle, have been carried out for this purpose and the results are presented in this paper. Trajectories and flight data for a range of approach techniques are presented and conclusions are drawn as to the potential benefits and issues of using such techniques for SUAV landings. In particular, the stability of the vehicle on entry to a deep stall was noticeably improved through the use of symmetric inboard flaps (crow brakes). Spiral descent profiles investigated, including spin descents, produced faster descent rates and further reduced landing space requirements. However, sufficient control authority was maintainable in a spiral stall descent, whereas it was compromised in a full spin.

Integrated multi‐agent system framework: decentralised search, tasking and tracking

This study is concerned with integrated target search, tasking and tracking using multiple fixed-wing unmanned air vehicles (UAVs). The problem is to design control logic and optimise flight paths for UAVs. The fixed-wing UAVs are required to cooperatively search the potential targets in the area of operation (AO) and keep monitoring and tracking the found targets according to a certain predefined minimal revisit time. Each UAV can only communicate with its neighbours and also flight autonomy is designed for individual UAVs. High level control logic based on finite state automaton model, integrating the four modes of operations, that is, take-off mode, fly-to-AO mode, search mode and tracking mode, is developed. For the target search, an improved decentralised coverage based algorithm is designed for fixed-wing UAVs. Task assignment is developed based on contract net protocol. For the target tracking, the optimal paths are derived for the UAVs to continuously monitoring multiple static targets. The developed integrated multi-agent system is evaluated by a hybrid three-dimensional simulation system using a real miniature fixed-wing UAV dynamic model.

Fixed Wing UAV Path Following in Wind With Input Constraints

This paper considers the problem of fixed wing unmanned air vehicles following straight lines and orbits. To account for ambient winds, we use a path following approach as opposed to trajectory tracking. The unique feature of this paper is that we explicitly account for roll and flight path angle constraints. The guidance laws are derived using the theory of nested saturations, and explicit flight conditions are derived that guarantee convergence to the path. The method is validated by simulation and flight tests.

Trajectory Tracking Control for Micro Unmanned Aerial Vehicles

This article considers the problem of trajectory tracking control for a micro fixed-wing unmanned air vehicle (UAV). With Bank-to-Turn (BTT) method to manage lateral deviation control of UAV, this paper discusses the outer loop guidance system, which separates the vehicle guidance problems into lateral control loop and longitudinal control loop. Based on the kinematic model of the coordinated turning of UAV, the aircraft can track a pre-specified flight path with desired error range. Flight test results on a fixed-wing UAV have indicated that the trajectory tracking control law is quite effective.

Optimal trajectory planning for path convergence in three-dimensional space

This article addresses the problem of determining the shortest path that connects a given initial configuration (position, heading angle, and flight path angle) to a given rectilinear or a circular path in three-dimensional space for a constant speed and turn-rate constrained aerial vehicle. The final path is assumed to be located relatively far from the starting point. Due to its simplicity and low computational requirements the algorithm can be implemented on a fixed-wing type unmanned air vehicle in real time in missions where the final path may change dynamically. As wind has a very significant effect on the flight of small aerial vehicles, the method of optimal path planning is extended to meet the same objective in the presence of wind comparable to the speed of the aerial vehicles. But, if the path to be followed is closer to the initial point, an off-line method based on multiple shooting, in combination with a direct transcription technique, is used to obtain the optimal solution. Optimal paths are generated for a variety of cases to show the efficiency of the algorithm. Simulations are presented to demonstrate tracking results using a 6-degrees-of-freedom model of an unmanned air vehicle.

Real-time approaches to the estimation of local wind velocity for a fixed-wing unmanned air vehicle

Three real-time approaches to estimating local wind velocity for a fixed-wing unmanned air vehicle are presented in this study. All three methods work around the navigation equations with added wind components. The first approach calculates the local wind speed by substituting the ground speed and ascent rate data given by the Global Positioning System (GPS) into the navigation equations. The second and third approaches utilize the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), respectively. The results show that, despite the nonlinearity of the navigation equations, the EKF performance is proven to be on a par with the UKF. A time-varying noise estimation method based on the Wiener filter is also discussed. Results are compared with the average wind speed measured on the ground. All three approaches are proven to be reliable with stated advantages and disadvantages.

Study of Low Cost Micro Autopilot for Fixed-Wing UAV

From the cost-effective viewpoint of low cost Bank-to-Turn (BTT) Unmanned Air Vehicles (UAV) and target drone, a low cost flight control system, with the fewest number of sensors, is studied in this paper for the fixed-wing UAV. The structure of the control system is described which is able to estimate necessary information to provide stabilization and guidance for a small fixed wing BTT UAV. The practical flight control system structure and control law for roll hold loop, altitude hold loop, trajectory tracking loop are designed based on the sensor configuration with only a MEMS rate gyro, a MEMS pressure sensor and global positioning system (GPS) receiver only. A prototype low cost autopilot is trial-produced to control a typical UAV. The Experimental results show the effectiveness of navigation and control methods of f the proposed methodology.