Top PDF Autonomous Vertical Recovery of Fixed Wing Unmanned Aerial Vehicles

Autonomous Vertical Recovery  of Fixed Wing Unmanned Aerial Vehicles

Autonomous Vertical Recovery of Fixed Wing Unmanned Aerial Vehicles

Autonomous recovery of a fixed wing UAV using vertical flight hails from research into vertical take-off and landing (VTOL) aircraft, although this application is focused only on the landing portion of the technology. The first recorded evidence of humans contemplating heavier than air VTOLs similar to helicopters we know today was Leonardo da Vinci’s helicopter drawings from 1493 [5]. Approximately 450 years later, early rotary wing helicopter prototypes would take to the air in leaps and bounds. While the roots of fixed wing aircraft technologies can be easily traced to its respective inventors, the helicopter has a more muddied history with many inventors [6]. The modern helicopter design we know today is largely attributed to Igor Sikorsky and consisted of a main lifting rotor with cyclic controls for pitch and roll control with a sideward thrusting tail rotor for anti-torque and yaw control [6]. With production versions appearing in 1941, applications for the reliable VTOL aircraft were military focused. The ability of the helicopter to take off and land without a prepared runway was an advantage for military applications like giving the ability to drop troops in conflict areas. To overcome the inefficiencies of rotary wing flight while maintaining the launch and recovery benefits afforded by VTOL, starting in the 1950’s, aircraft manufacturers perused hybrid designs known as tilt-wing and tilt-rotor designs [7]. While helicopters are more mechanically complex than airplanes, tilt wing/rotor aircraft are significantly more complex than helicopters. The tilt wing design had wing mounted engines that would articulate vertically by rotating the entire wing (a type of which is depicted in Figure 4).
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Disturbance rejection flight control for small fixed-wing unmanned aerial vehicles

Disturbance rejection flight control for small fixed-wing unmanned aerial vehicles

The simulation scenario requires the UAV to reduce the airspeed from 35m/s to 25m/s while maintaining a constant height at 100m. Since the airspeed spans a large range across the flight envelope, the nonlinearity of the dynamics will be excited. The tracking results of this case study are given in Fig.7 and 8, which manifest that the controller design based on the NDI technique is able to deal with the nonlinear dynamics and provide stability. However, the baseline NDI controller in this scenario demonstrates notable tracking errors especially on the height channel due to the combination of the external disturbance and internal uncertainties. The NDI controller with integral action gives a better tracking accuracy than the baseline controller. Nevertheless, compared to DOBC it yields a more oscillatory outputs and its correction is much slower as it needs the error signals to accumulate adequately to take actions. This becomes more obvious at 60s of the simulation when the vertical wind speed starts to change from −2m/s to 2m/s. The corresponding height output under the integral NDI control results in a large deviation from the reference signal, whereas the output under DOBC quickly recovers to the reference signal because it exploits the estimated disturbances in a feed-forward fashion.
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Cooperative Control for Landing a Fixed-Wing Unmanned Aerial Vehicle on a Ground Vehicle

Cooperative Control for Landing a Fixed-Wing Unmanned Aerial Vehicle on a Ground Vehicle

The intention for the final application is that both the UAV and the UGV will be fully autonomous vehicles. The low availability of a large enough autonomous ground vehicle motivated the choice of instead using a semi-autonomous vehi- cle for these initial proof-of-concept tests. In this solution a human driver is executing control commands provided by the ground vehicle controller through a graphical interface. Having a human actuator in the loop introduces several possible challenges. First of all there will be a natural time delay corresponding to the reaction time of a human. Secondly, it is difficult to make the human reliably follow these commands without adding extra control himself. Another possible complication is that a human could unconsciously take other things into consideration when applying control, such as the sound of the UAV motor or in- tuition of how the experiment should go. It might also be more difficult to have advanced control settings such as simultaneously instruct the driver to accelerate and steer, since the driver will most likely want to look at the road as well.
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Autonomous landing of a fixed-Wing unmanned aircraft with partialwing and stabiliser losses

Autonomous landing of a fixed-Wing unmanned aircraft with partialwing and stabiliser losses

Unmanned aerial vehicles (UAVs) are becoming more popular for military as well as commercial use. Removing the human aspect from the machine results in a system that can operate in more harsh environments and for longer periods. A few examples of the ever-expanding applications of UAVs are notably combat missions, search and rescue, disaster management, surveying and also as delivery systems. A UAV is typically adapted for a specific application, which allows it to perform at its best for the given mission. Each application introduces new uncertainties and complications that need to be considered in the development process. A fully autonomous UAV is capable of performing autonomous take-off, navigation and landings, which all form part of the typical aircraft phases or tasks seen in Figure 1.1. Of all these tasks, landing the aircraft is the most difficult. Landing typically entails aligning the aircraft with the runway, reducing the aircraft’s airspeed (which is kept well above stall speed) and following a glide path at a certain sink rate until touchdown. For a UAV to successfully perform a fully autonomous landing, strict longitudi- nal and lateral-directional control are required to ensure that the aircraft follows the desired glide path and stays within the runway bounds while approaching the touchdown point. A significant amount of research continues to go into the development of UAVs and how they can reliably in- tegrated into military and civil airspace. Human safety is one of the most important factors that needs to be considered before sending a UAV into missions.
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Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-objective Genetic Programming

Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-objective Genetic Programming

tracking control in simulation. They showed that the evolved controller was able to handle un- certainties and disturbances. Marin, Radtke, Innis, Barr, and Schultz [49] evolved a controller for a UAV of an unspecified type. They evolved a set of rules to reactively control a UAV’s flight based on target detection. Their experiments were only in simulation, the movement of the UAV was grid-based, and the UAV could move in any direction at every time step. Be- cause of the unrealistic nature of the simulation, it would have be difficult to control real UAVs with the evolved controllers. In related work, Wu, Schultz, and Agah [79] evolved a control scheme for micro air vehicles. Their evolved control system was distributed. Each vehicle had its own controller, though all controllers were identical. Rule sets were evolved to control the UAVs. Like the experiments in [49], only simulation was used, simulation was unrealistic, and no testing on real UAVs was attempted. Meyer, Doncieux, Filliat, and Guillot [54] evolved a neural network control system for a simulated blimp. Unlike rotary wing and fixed wing UAVs, a blimp is very stable and easy to pilot. The goal of the research was to develop controllers capable of countering wind to maintain a constant flying speed. The evolved control system was only tested in simulation.
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Fault-tolerant flight control for a fixed-wing unmanned aerial vehicle with partial horizontal and vertical stabiliser losses

Fault-tolerant flight control for a fixed-wing unmanned aerial vehicle with partial horizontal and vertical stabiliser losses

The first flight tests were for the safety pilot to determine whether the aircraft could be controlled under the specified damage condition. This test was conducted without any avionics on board, with just the ‘bare-bones’ RC system. This allowed the safety pilot to provide feedback on the performance of the aircraft under the different damage cases and reduce the risk of losing avionics should a problem arise. If the pilot was confident that they could take off, land and recover the aircraft if necessary under the damage condition, the release mechanism could be tested in flight. This was also tested incrementally (one surface at a time) in order to provide insight about the transition process. When the release mechanism was tested and working as desired, the avionics package was installed in the vehicle, and the controllers were tested on the healthy aircraft. The controllers were then tested on the separate damage configurations (partial horizontal and partial vertical stabiliser) independently and finally with the combined stabiliser loss. After a successful damaged configuration autonomous flight, the final in-flight transition from healthy to damaged aircraft was tested to investigate the performance of the controller during the transition.
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Auto-Landing System for Fixed-Wing Unmanned Aerial Vehicle

Auto-Landing System for Fixed-Wing Unmanned Aerial Vehicle

microcontrollers, control systems and flight simulation software are widely available. These many recent developments make it possible to design a fixed- wing platform that can be operated autonomously. This thesis focused on the development of a UAV landing algorithm for use with a simple cost-effective platform that, with some added componentry and modifications, is capable of flying autonomously, locating the runway, and landing safely. The development of affordable and easy to pilot unmanned autonomous vehicles can be enabled by technologies as proven in this thesis. The availability of powerful development and analysis tools such as MATLAB and flight simulators contribute to
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Study and suppression of vibrations in rotary-wing Unmanned Aerial Vehicles

Study and suppression of vibrations in rotary-wing Unmanned Aerial Vehicles

equations of motion a series of assumptions are usually made: the vehicle is symmetric, the inertia-matrix is diagonal and invariant and the mass centre of the vehicle coincides with the origin of the body-fixed reference system. There are authors that model the full motion of the vehicle, i.e., with six degrees of freedom, three rotational and three translational, [20–28]. Also a simplified version of this model is quite common; since the lateral and longitudinal motion is determined by the angular position of the vehicle and the total lift forces, only the angular degrees of freedom, [29–35] or the angular motion plus the altitude, [36–38], are considered. Some authors have as well modelled variable configurations, such as a tilt-wing aerial vehicle that is capable of flying in horizontal and vertical modes, [39–41]. Different authors consider more details on the dynamics involved on the quadrotor motion than others; the more simple dynamic model, that can be found in control oriented research, only considers the motion of the vehicle body as a function of the external aerodynamic forces, not considering the gyroscopic or inertial effects, [42–44]. Other authors go one step further by considering the gyroscopic effects acting on the structure [45–47]. However, in order to include all the nonlinearities present in the quadrotors’ motion, the gyroscopic [48–52] and inertial effects [53] of the rotors need to be considered.
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Advanced take-off and flight control algorithms for
fixed wing unmanned aerial vehicles

Advanced take-off and flight control algorithms for fixed wing unmanned aerial vehicles

The aim of the UAV group in the Electronic Systems Laboratory (ESL) at Stellenbosch University is to further UAV research to push the boundaries of unmanned flight. Before that could be accomplished, a foundation of basic flight controllers for fixed wing aircraft had to be laid down. Previous research such as autonomous Take-off and landing (ATOL) [ 11 , 12 ], basic flight control with Waypoint navigation [ 13 ], aerobatic flight [ 18 ] and hover control for vertical Take-off and landing (VTOL) [ 26 ] have succeeded in creating this foundation. This has led to more advanced flight control which include the expansion of the flight envelope of UAVs [ 19 ], allowing the aerodynamic optimisation of airframes by eliminating stability criteria [ 24 ], precision landing [ 25 ] and improving flight safety through stall prevention [ 27 ].
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A Data Driven Adaptive Method for Attitude Control of Fixed Wing Unmanned Aerial Vehicles

A Data Driven Adaptive Method for Attitude Control of Fixed Wing Unmanned Aerial Vehicles

The performance of the attitude controller of a fixed-wing UAV determines the quality of its autonomous flight. Some accurate mathematical model-based me- thods were proposed for the attitude control, for example, PID and LQR me- thods (linearized model based) [1] [2] [3], adaptive control method [4], feedback linearization method [5] [6] and nonlinear dynamic inversion method [7] [8]. For some other methods, perturbation within a small range is allowed [9] [10] or How to cite this paper: Chen, M.L. and

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Developing a Software for Autonomous
Unmanned Aerial Vehicle

Developing a Software for Autonomous Unmanned Aerial Vehicle

Unmanned Aerial Vehicle systems (UAV) become very attractive for various commercial, industrial, public, scientific and military operations. Latest developments in the field of navigation systems have led to miniaturized boards which integrate GPS, enabling to fly UAVs in an autonomous way. These new technologies allow low cost navigation systems to be integrated in helicopters’ models, though the advantage of small size positioning and orientation sensors come with low prices. Nevertheless, the combination of GPS/INS sensors with image data for navigation represents a key factor to achieve more precise and reliable results with respect to manually controlled UAVs. Previous work has focused on the engineering side of UAVs and ignored the software development cycle included in this process. One of the important developments is the Draganflyer X6 UAV helicopter, designed by Draganfly Innovations Inc. [2] for aerial photography and ideography. TiaLinx designed and developed the most advanced imaging technology and integrated sensor clusters for the high-end security and surveillance applications [3]. Other UAVs like the fixed wing UAVs currently used by the military are large, expensive, special purpose vehicles with limited autonomy [10]. At the other end of the spectrum, there are small UAVs (less than six foot wingspan) and micro air vehicles (MAV) (less than one foot wingspan) [4, 5, 6, and 7]. This paper proposes a software development model that utilizes an innovative design with differential thrust inspired by the above mentioned systems. This design was used in a system that allows the helicopter to maneuver in a quick and
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Autonomous landing of a fixed-wing unmanned aerial vehicle onto a moving platform

Autonomous landing of a fixed-wing unmanned aerial vehicle onto a moving platform

Figure 7.48: NSA spike for a moving platform landing and recovery during HIL simulations. was simply commanded to track the trim altitude after the touchdown was detected, while the roll angle was commanded to remain wings-level. It was decided to not command a high pitch angle to quickly increase angle of attack and therefore gain additional lift during this procedure. The resulting large pitch rate may cause the tail to strike the platform, thereby damaging the undercarriage, elevator or the servo motors. Figure 7.50 shows the large deviation in airspeed during the touch-and-go manoeuvre. The reason for this is twofold. Firstly, the aircraft experiences a large backwards force due to dynamic friction when the wheels come into contact with the platform surface, especially since the wheels are prone to bending under large downwards forces. Secondly, the large altitude step also causes the controllers to exchange some kinetic energy for potential energy, thus lowering the airspeed. The specific energy integrator may not wind down fast enough after the altitude is reached, therefore causing the overshoot in airspeed. The manoeuvre is however performed successfully and deemed acceptable for practical flight.
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Autonomous landing of a fixed-wing unmanned aerial vehicle using differential GPS

Autonomous landing of a fixed-wing unmanned aerial vehicle using differential GPS

In 2008 Peddle submitted his thesis for a Doctorate in Philosophy in engineering, with the title of “Acceleration-based manoeuvre flight control systems for unmanned aerial vehicles” [1]. This work became a cornerstone for many of the projects in the ESL, including this present project. The focus of Visser's master's dissertation [11] on the topic of ATOL is the autonomous precision landing of a UAV by incorporating research from [1] and vision-based sensors. Visser, like Roos, made use of a standard GPS to guide the UAV onto the final approach of the runway. During the landing phase, vision sensors were used to obtain accurate position and altitude data. The camera system relied on beacons that were placed near the landing target to obtain reliable data. The camera system that Visser developed was tested successfully. Unfortunately, a radio frequency (RF) failure on the UAV resulted in the destruction of the vehicle, preventing a successful landing. De Hart [12] used the same aircraft to extend Peddle's research by implementing advanced take-off and control algorithms for fixed-wing UAVs.
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Securing Embedded Systems for Unmanned Aerial Vehicles

Securing Embedded Systems for Unmanned Aerial Vehicles

Weight constraint is also a very important factor for every aerial vehicle which largely influences the design process. This is more vital for UAVs due to their small size. Weight does not only affect the UAV’s speed but also has very big impact on power budget and overall performance of the UAV. Platform designers need to consider all factors to make the UAV as light as possible. Consequently the system for this project must fit into a regularly sized UAV. As UAVs’ purposes differ in many areas, UAVs’ weight also has very big range. For example, the Arcturus UAV, which is used for simple surveillance, has maximum weight of 75 pounds (about 35 kilograms), while the MQ-4C Triton that is used for marine intelligence gathering has a gigantic weight of more than 14,000 kilogram [52] [53]. However, as this project is in smaller scale, it is more focused toward small size UAVs that range between 20 kilograms and 100 kilograms.
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Terrain Mapping From Unmanned Aerial Vehicles

Terrain Mapping From Unmanned Aerial Vehicles

In land surveying, a number of conventional devices have been used in producing terrain mapping particularly DTM and DSM. There are such as total station [1], global positioning system (GPS) [2], light detection ranging radar (LiDAR) [3-4], manned aircraft [5-6], terrestrial laser scanning (TLS) [7] and remote sensing [8-9]. However, despite have been benefitted many, these approaches suffer from certain limitations particularly in terms of time consumption, usage and costing. The issue is much more serious in the tropical regions which are known persistently covered with clouds especially during monsoon seasons, making it difficult to capture high-quality images even by using remote sensing satellite technology. Meanwhile, GPS survey requires a lot of time to establish high-density points in the study area. This is because GPS survey method measures discrete point on the surface. Therefore, this method is not practical for projects allocated with limited budget and time [3]. Terrain mapping using LiDAR and manned aircraft are very costly but has low ground resolution and limited time frame hence, rather impractical to be used for low altitude and small area surveying. Recently, UAV has been given a great attention in many applications including terrestrial terrain mapping, mainly, due to its low cost and practicality [10-11]. A UAV is commonly integrated with autopilot technology that enables semi or full autonomous navigation and image acquisition capabilities [1]. The image acquisition capabilities enable Earth terrain to be mapped and modelled to produce orthophoto. Orthophoto is an aerial photograph that has been geometrically rectified with appropriate scale and curvature, which has been considered as a vital element in the field of photogrammetry. Besides orthophoto, images acquired from UAV can also be used to generate Digital Terrain Model (DTM), which is the spatial terrain elevations of bare-earth, DTM can be utilized
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Unmanned aerial vehicle abstraction layer: An abstraction layer to operate unmanned aerial vehicles

Unmanned aerial vehicle abstraction layer: An abstraction layer to operate unmanned aerial vehicles

Additionally, it is essential to integrate the UAV soft- ware frameworks with simulation environments in order to switch from simulated to real platforms with minor efforts. Due to the fact that UAVs are more sensitive and fragile than common ground vehicles, preliminary simulations to verify the correct operation of the whole system become even more critical. For that, there are widespread simula- tors, such as Gazebo, 10 V-REP, 11 or AirSim, 12 that allow researchers to test UAV and multi-UAV systems, for task allocation 13 or path planning algorithms, 14 among others. MATLAB Simulink has also been proposed as a rapid pro- totyping environment for UAVs. 15 Simulators with realistic 3-D engines (e.g. AirSim) can also be used for testing computer vision algorithms.
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Multi-agent pathfinding for unmanned aerial vehicles

Multi-agent pathfinding for unmanned aerial vehicles

This section introduces the dynamic real-time rerouting problem applied to UAVs, provides a generic formalization, and presents the solution created for this thesis. The word route, as opposed to path, is used because it is the term most widely used by the UAV community, Dynamic inflight UAV rerouting is a special case of online mapf applied to UAVs. In the UAV ecosystem, unexpected events may occur in the airspace, forcing autonomous UAVs to deviate from their designated routes. A UAV may experience engine failure and need to safely maneuver to a nearby landing location. A medical helicopter may need to occupy the same route as a UAV. Inclement weather or police activity may cause a large portion of the airspace to be temporarily closed. These are all events that dynamically add an obstacle to the mapf problem at runtime. The terms obstacle and no-fly zone will be used interchangeably. A UAV experiencing engine failure can be seen as an obstacle because it will not be forced to change its path and therefore must be avoided by other UAVs. Every UAV has an operation. An operation consists of a start location, s i , service location,
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Behavior Flexibility for Autonomous Unmanned Aerial Systems

Behavior Flexibility for Autonomous Unmanned Aerial Systems

The Unified Behavior Framework (UBF)[1] provides a design strategy to create modular and extensible behavior-based robotic agents. For many years, behavior- based approaches have produced robust and responsive intelligent agents. However, in robotic applications, behavior logic is often inextricably tied to the underlying con- trol mechanisms making reuse, modification, and extension of behaviors difficult. The UBF was developed to address these limitations by abstracting behavior logic from the underlying robotic controllers. Behavior abstraction allows developers to easily reuse and modify behaviors to extend an existing behavior-based controller or to quickly create a new controller. The efficacy of the UBF was demonstrated through successful implementation on a variety of platforms [22, 23, 1]. However, overall agent behavior-flexibility was limited by their ground-based platforms. Open-source robotic software frameworks (RSF), such as the Robot Operating System (ROS) [21] and flight controllers such as the PX4 autopilot [20], currently offer a rich appli- cation programming interface (API) for UAS applications that could facilitate the integration of UBF behavior logic with a variety of sensors and physical vehicle types [24, 25, 26]. In addition to being the first use of the UBF on a UAS before, the inte- gration of these technologies offers tremendous potential as behavior-flexible platform for autonomous agents on UAS.
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Optimization Of Unmanned Aerial Vehicle Wing Structure

Optimization Of Unmanned Aerial Vehicle Wing Structure

The wing structural design problem is composed into two levels in a hierarchical structure at the first level, the wing configuration is completely made up of isotropic material and the following design parameters were investigated such as number of stringers, number of ribs. The second level wing substructure (stringers, ribs) is made of isotropic material and the skin panel is made of composite material. For composite material, the work covers the investigation of the effect of changing 3 type of composite material. Then from the results we take the optimum design.
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Autonomous Unmanned Aerial Vehicle In Search And Rescue

Autonomous Unmanned Aerial Vehicle In Search And Rescue

Because the drones must sweep a large area, an aeroplane is the best suitable airframe. To generate less drag and increase stability; long, slender and thin- as-possible wings are recommended. To achieve aerodynamic stability, non-swept wings and a small positive dihedral angle is also advised. The wings should be attached on top of a slender and small-as-possible fuselage. However, due to the difficulties in landing and vulnerabilities related to this, a flying wing which obey the same design requirements, may be a better choice. A prototype for a flying wing made in expanded polypropylene was put together and tested. It proved to be resilient, able to withstand significant abuse, quickly recover to its former structure and be repaired in minutes. Highly convenient for various landing areas.
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