Top PDF 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

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

In manual flight mode where the inputs are directly given by a pilot mov- ing the control surfaces, feedback control can be used to improve the handling qualities of the aircraft by shaping the closed loop response. This makes the air- craft easier to fly by damping oscillatory terms and stabilizing possibly unstable modes. In an unmanned aircraft on the other hand, inputs are given in terms of desired path, altitude and velocity. We are not directly interested in the dynami- cal response from the control surfaces, but rather how well the aircraft can follow given reference values. Still, looking at and compensating for the stability and damping of these modes and trying to get a good control surface deflection to state response is a good staring approach for a completely automatic controller. Flight control is to a large extent based on cascade control, where the inner loops are successively closed to attain a desired performance. This requires both system knowledge and experience from the control designer when choosing the structure, and so making or changing an existing controller can become a large and time consuming effort. With increasingly complex flight systems, modern control techniques are becoming more popular, with methods like eigenstructure assignment, LQR, and robust control being among some of the techniques that have been used in aircraft control systems.
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Assessment of photogrammetric micro fixed-wing unmanned aerial vehicle (UAV) system for image acquisition of coastal area

Assessment of photogrammetric micro fixed-wing unmanned aerial vehicle (UAV) system for image acquisition of coastal area

Table 3 shows the comparison of check points between coordinates from ground survey (i.eGlobal Positioning System (GPS)) and coordinates obtained from image processing software, where the calculated RMSE is ± 0.018, ± 0.013 and ±0.034 meter (<1 meter) for coordinate x, y and z respectively. It can be seen that the accuracy can be achieved using micro fixed-wing UAV system based on the one strip of digital aerial photograph for coastal area. The smaller the RMSE calculated, the higher the accuracy of orthophoto produced. The smaller the RMSE, the better orthophoto could be produced. It can be concluded that the higher the GCPs was, the better the RMSE. Hence, the accuracy of orthophoto is influenced by the RMSE value.
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Position Control of an Unmanned Aerial Vehicle From a Mobile Ground Vehicle

Position Control of an Unmanned Aerial Vehicle From a Mobile Ground Vehicle

In [37], the authors propose a nonlinear control technique for landing a UAV on a moving boat. Here the attitude dynamics are controlled using fast time scale with guaranteed Lyapunov stability. The authors present the simulation results for the same with guaranteed robustness for bounded tracking errors in the attitude con- trol. The method relies on time scale separation of attitude and position control which makes the controller algorithm computationally expensive and dependent on the communication protocol used. A tether is used for communication between the boat and UAV. Similarly, authors in [28] work to derive a control technique for coor- dinated autonomous landing on a skid-steered UGV. First a linearized model of the UAV and the UGV are developed and then control strategies are incorporated in the closed loop to study the coordinated controller action. In this work, the non-linearities of the quadcopter are overlooked.
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A smart Neural Network Based Algorithm for Landing Control of Autonomous Unmanned Aerial Vehicle

A smart Neural Network Based Algorithm for Landing Control of Autonomous Unmanned Aerial Vehicle

1177 | P a g e included a method referred as deep stall landing, the advantage of the proposed method is that it can be used in landing in small space, where the UAV is in a deep stall when the angle of attack is greater than the stall angle, which cause UAV to lose height fast, an accurate control algorithm is a preliminary requirement for such method, delta wing UAV was used. while the another in work [6], has used the same optimal control; continuous model predictive control (MPC) method for landing control of UAV, the algorithm assumes the controlled plant as a multi-input system (an optimal constraint problem), in aspect of speed and descent rate which are basic concern during the landing final approach phase, a mathematical module of UAV was has been derived and an optimal problem was formulated and simulated the obtained results was compared with results obtained from simulating the same model with PID controller, and the MPC has given the best results, the approach is quite useful for autonomous landing but neglected the first phase of the landing .
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Capabilities of low cost and fast image acquisition using micro fixed-wing unmanned aerial vehicle system

Capabilities of low cost and fast image acquisition using micro fixed-wing unmanned aerial vehicle system

Fast image acquisition is most important part for societal impact of a developing country. This paper aims to demonstrate the potential use of micro fixed wing unmanned aerial vehicle (UAV) system attached with high resolution digital camera for coastal mapping. In this study, a strip of aerial images of simulation models of coastal area was captured using a high resolution compact digital camera known as Canon Power Shot SX230 HS and it has 12 megapixel image resolution. The low cost supplying of micro fixed wing UAV can be used in various applications including mapping coastal area. The UAV equipped with an autopilot system and automatic method known as autonomous flying, can be utilized for data acquisition. In this study, the UAV system has been employed to acquire aerial images of a simulation model at low altitude. From the aerial images, photogrammetric image processing method is completed to produce mapping outputs such a digital terrain model (DTM), contour line and orthophoto. In term of the accuracy, of measurement, a milimeter- level is reached by ground control point (GCP) and check point (CP) using conventional ground surveying method (i.e accurate Global Positioning System (GPS)). For accuracy assessment, the coordinates of the selected points in the 3D of stereomodel were compared to the conjugate points observed using GPS and the root mean square error (RMSE) is computed. From this study, the results showed that the achievable RMSE are ± 0.018m, ± 0.013m and ± 0.034m for coordinates X, Y and Z respectively. It will anticipate that the UAV will be used for coastal survey and improve current method of producing with low cost, fast and good accuracy. Finally, the UAV has shown great potential to be used for coastal mapping and others applications that require accurate results or products using high resolution camera.
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Event-Based Visual-Inertial Odometry on a Fixed-Wing Unmanned Aerial Vehicle

Event-Based Visual-Inertial Odometry on a Fixed-Wing Unmanned Aerial Vehicle

The limited availability of event-based cameras, due to their current high cost, is an obstacle to widespread investigations into applications for these unconventional sensors. To alleviate this issue, several research organizations produced event-camera datasets, supporting performance evaluation of available algorithms. The Robotics and Perception Group (RPG) at INI has made available a dataset with a couple dozen scenarios collected with a DAVIS camera. They include indoor and outdoor environments viewing simple shapes, varied surfaces, or just a simple office. The datasets include both simple rotational or translational movement as well as more erratic rapid movements. All collections include events, camera calibration informa- tion, IMU, and the frame images from the integrated CCD camera. For the indoor collections in controlled environments, they also include ground truth data from a motion capture system [3].
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A neural network based landing method for an unmanned aerial vehicle with soft landing gears

A neural network based landing method for an unmanned aerial vehicle with soft landing gears

Flying animals, such as birds, can perch on trees, poles, and other non-flat surfaces which are not suitable for UAV landing. Equipped with this soft landing ability, birds may occupy a high vantage point [10]. They can stay there to search, forage, and rest for an extended period of time [11,12]. As a high-challenge landing approach, perching refers to precise landing pose control and the effectiveness of energy absorption [13]. Cory and Tedrake, of the MIT Computer Science and Artificial Intelligence Laboratory, have analyzed and presented fixed-wing precise drone landing tests and demonstrated that angles of attack are critical factors during the touchdown procedure [14]. Mirko Kovac from the Imperial College of London designed an aerial robot equipped with soft shock absorbers which can land on a convex surface with diameter longer than robot’s body length [15]. Subsequently, many inspired mechanical devices have been investigated to test this hypothesis. For example, a pitch-up touchdown sequence, consisting of distance detection, UAV nose up, and soft
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Automated landing of a quadrotor Unmanned Aerial Vehicle on a translating platform

Automated landing of a quadrotor Unmanned Aerial Vehicle on a translating platform

Attention is now shifted towards other aerodynamic effects that play a role while landing a rotorcraft. An aerodynamic effect that is relevant to automated landings is the IGE. According to Schmaus et al., IGE is a term used to describe the changes in performance that rotorcraft and fixed-wing aircraft experience as they approach the ground [23]. Schmaus et al. further state that the IGE is often viewed as an increase in thrust for a constant power, which can be beneficial in some applications, but not for an automated landing where the aircraft dynamics are changing. When a rotorcraft is subject to IGE conditions, the wake of a helicopter rotor interacts with the ground and causes significant perturbation to the flow near the rotor blades, as well as the rest of the craft [24]. IGE therefore effectively disturbs the aircraft, which is not favorable while the aircraft is translating near the platform. The IGE, to its full extent, is not applicable to this project. The platform which is avail- able in the ESL, and which the aircraft will be landing on, is perforated. The perforated surface allows the wake from the rotors to partially pass through the platform. The effect from coming close to the platform is therefore decreased, but not completely absent. As a consequence, it might be worthwhile to investigate this phenomena separately.
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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

This flight test was used to obtain telemetry data for the aircraft under RC flight. This provided information that gave confidence in the sensors and also provided insight into how the different damage configurations affected the dynamics of the aircraft. After the first few flight tests, it was safe to say that the aircraft can be flown in the different damage configurations. The avionics were reinstalled on the aircraft and calibrated accordingly. The full estimator was enabled and the telemetry logged for investigation after the flight. The healthy aircraft was flown in its symmetric configuration to provide a baseline of the behaviour of the aircraft. The take-off and landing were both performed on the healthy configuration. The aircraft was then flown with partial horizontal and partial vertical stabiliser respectively and finally combined. For each of these flights, the aircraft took off, executed manoeuvres and landed in the respective damage configuration. A flight with a transition from healthy to damaged was also conducted to provide insight into the dynamics of the aircraft as its model changed. The transition flight had the safety pilot take off with the healthy aircraft. During flight, the transition was triggered. The safety pilot then landed the aircraft in the damage configuration. Under RC testing, the safety pilot was constantly making corrections to the aircraft to ensure that it stayed in the air. As a result of these constant corrections, small events, such as the transitions or doublets, were not observable from the sensor measurements.
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Predictive control of an unmanned aerial vehicle with a time-variable mass

Predictive control of an unmanned aerial vehicle with a time-variable mass

Two other interesting master theses, [17] and [10], whose authors cooperated, explored the planning aspect of a trajectory in order to reach the optimal release point. However, these theses worked on a fixed-wing aircraft, the payload was supposed to be released from high altitudes and therefore the heat exposure criterion did not affect that planning. In addition, contrary to our case, the payload mass does not play a role in the planning because it is negligible compared to the vehicles mass. Similarly, [11] describes an interesting trajectory planning that manages to converge in about two seconds but it has no variation of any kind in any of the model parameters and the UAV is a fixed wing aircraft.
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Unmanned Aerial Vehicle (UAV) Cooperative Mission Planning

Unmanned Aerial Vehicle (UAV) Cooperative Mission Planning

Unmanned Aerial Vehicle (UAV),is a unmanned plane to use wireless remote control equipment and own program control device to control oneself, whichinstalls Autopilot,program control device, etc.Ground control station personnel use transmitting station device to tracking, positioning,remote control, telemetry and digital transmission for UAV [1]. UAV can take off like ordinary plane under the wireless remote control or be used launching cradle to blast offin the military, the UAVs not only are widely used and low cost, but also has the advantages of no casualties risk and great viability [2]. UAV plays a very important role in the modern war, and has broad prospects in the civil field.
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Ground vehicle detection and classification by an unmanned aerial vehicle

Ground vehicle detection and classification by an unmanned aerial vehicle

constituted by an IMU (Inertial Measurement Unit), a GPS (Global Positioning System), an energy manager and a data link. This system keeps the stability according to the aerial robot position and communicates with a ground station by the data link (receptor/robot). The ground station communicates to an iPad by bluetooth connection, in which it is installed the DJI software. This software provides several functionalities and configurations, such as to follow a pre-specified route, autonomous departing and landing, control by virtual joystick (on iPad the screen), to start, change and exit missions, to set the altitude, speed, hover time, and other settings. In Figure 4 is showed a screen of software ground station DJI.
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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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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

In simulation, it was found that the longitudinal control system was unable to maintain the airspeed of 16m/s and flight path angle of -8.2 ◦ commanded for the first steep glide slope. The behaviour under these commands is discussed in more detail in Section 5.3. The control system settled to an undesired steady state, which saturated as the glide slope became steeper. Addi- tionally, the second glide slope proved to negatively affect the settling time of the controllers, thereby reducing the landing accuracy. At this point, the approach of using a steep glide slope followed by a shallower glide slope was reconsidered. It was also practically observed that the runway obstruction for stationary landings could be avoided and that flying at an increased airspeed will improve the natural stability of the aircraft by it aligning its velocity vector into incoming airflow. It was therefore decided to merge the two sequential glide slopes into a single glide slope which more closely resembles the standard landing procedure. The aircraft could now also be put on this glide slope at an earlier point, allowing more time for it to reach a trim settling value. The single glide slope approach is shown in Figure 3.8. The glide slope angle is calculated with
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Autonomous take-off and landing of a fixed wing unmanned aerial vehicle

Autonomous take-off and landing of a fixed wing unmanned aerial vehicle

The tests will range from hardware (sensor) testing to controller testing for full autonomous flight and runway ground speed and yaw rate control. The hardware test will [r]

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Auto-Landing System for Fixed-Wing Unmanned Aerial Vehicle

Auto-Landing System for Fixed-Wing Unmanned Aerial Vehicle

scope, a combination of an off-the-shelf auto-pilot controller and a dedicated auto-landing controller were selected. This approach divided the tasks of flying and landing between the two controllers. The benefit of this approach was, first, to simplify the thesis development in order to focus on the landing portion only, and secondly, to optimize each controller capability for their dedicated task. The auto-landing controller was an ARM M3 based CPU, and it was equipped with appropriate interfaces such as pulse width modulation (PWM) to interact with servo motors typically utilized on unmanned aerial vehicle platforms. Additionally, this controller can interact with the auto-pilot over UART. The auto-pilot
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Development Of The Unmanned Aerial Vehicle In Hexacopter For Attitude Control

Development Of The Unmanned Aerial Vehicle In Hexacopter For Attitude Control

In contrast to fixed-wing and traditional helicopter UAVs, multi-rotors MAVs are much safer to operate, can easily hover above the target and able to fly at low altitude, are highly maneuverable and do not require complex mechanical control linkages. In addition, multi-rotors are more stable in calm condition and able to tune easily due to its higher frequency in vibration. However, the increasing of drone size and weight as well as its manufacturing value counts towards. Therefore, the hexacopter seem to be a good comparison and possibility manage one or more engine failure and increase the total payload. In particular, benefit of more rotors appear will provide more power and lift, that mean more time travel on fly.
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Design and Optimization of Wing Structure for a Fixed Wing Unmanned Aerial Vehicle (UAV)

Design and Optimization of Wing Structure for a Fixed Wing Unmanned Aerial Vehicle (UAV)

DOI: 10.4236/mme.2018.84017 255 Modern Mechanical Engineering and effective height ratio are considered purely from the structural point of view. In fact, the choice of structural load types is largely influenced by many factors in the overall layout of aircraft and needs comprehensive measurement. The first aspect is about internal layout of wing, such as retractable landing gear, large openings, etc. The second aspect is the relative position of the wing and the fu- selage, as well as the interior of the fuselage. The third aspect is the geometric parameters of the wing, such as swept wing, delta wing, etc.
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Design and Fabrication of an Unmanned Aerial Vehicle

Design and Fabrication of an Unmanned Aerial Vehicle

Achtelik et.al. [2] finished research on visual following control of quad copter using the stereo camera method. The development of a quad copter is controlled in perspective of visual input and estimation of inertial sensor. In this examination work, dynamic markers were finely planned to improve perceivability under distinctive perspectives furthermore to guarantee vigor towards aggravations in the photo based stance estimation. Additionally, position- and heading controllers for the quad were completed to show the limit of the framework. The exhibitions of the controllers were further improved by the use of inertial sensors of the quad copter. A shut circle control structure is successfully coordinated in this exploration.
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Tracking Control Design for Quadrotor Unmanned Aerial Vehicle

Tracking Control Design for Quadrotor Unmanned Aerial Vehicle

Dhaifullah 14 , et al. have proposed a modified backstepping control technique to reduce the control gain parameters by half as compared to the classical backstepping approach. The feedback linearization 15 coupled with a PD controller for a translational subsystem and backstepping-based PID controller for rotational subsystem has been used to improve the performance of quadrotor. The sliding mode control has been shown 17-22 to stabilize the quadrotor helicopter which can move it to any position with any yaw angle. An adaptive sliding mode controller has been developed 23 to improve performance and reliability, for handling aerodynamic parameter uncertainties and external disturbance. The main advantage of the nonlinear controllers is low sensitivity to plant variations and disturbances. However the sliding mode control has some drawbacks such as chattering effect, limited design freedom for designer with sliding function. The chattering effect 18-20 occurs due to the inclusion of the sign function in the switching control and due to the non-ideal behaviour of system. Extended sliding mode control method has been proposed 24-28 , to reduce the chattering effect, improve the performance, reliability for handling external disturbance and aerodynamic parameter uncertainties.
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