[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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222 K. Daniec et al. 3 Lockheed Martin Prepar3D Prepar3D® was mostly implemented by Lockheed Martin, but it was created on Microsoft ESP (Enterprise Simulation Platform), which was designed for the purpose of Microsoft Flight Simulator. Microsoft's program has been widely used, but it was limited only to control the flying objects, which also proved to be insufficient for current military purposes. To ensure the possibility of further product development, Lockheed employed several members of the ACES studio. In the future, Lockheed Martin is going to modify the Microsoft ESP to allow its use for military and civil services training in the event of any type of disasters. Prepar3D® flight simulator has a wide range of functionality. Three of them appear to be necessary and sufficient for prototyping control algorithms. The first of functionalities is object model configuration. Configuration of the model allows us for example to change many of the physical and geometrical parameters such as weight, size and wingspan. It also allows us to change dynamic parameters such as engine power, effectiveness and stability of ailerons or elevator deflection. Those allows us to parameterize object model, whose behaviour resembles the real object. There can be configured the sea, air and road traffic, that proves to be useful for prototyping the vision based algorithms, for example, for vehicle and ships tracking purpose. The simulation allows to create on-board cameras, which can be moved or zoomed both by an operator and also as well as an appropriate autonomous algorithm. It is possible to run multiple cameras at one time, not only in vision system, but also in thermo-vision, night-vision (fig. 3) and infrared vision systems. These virtual cameras can be placed (configured) in areas that correspond to the location of cameras in real UAV. Optionally it could be possible to acquire multispectral images [12]. This gives a wide possibilities to create different types of algorithms for object detection and obstacle avoidance. These algorithms can be further moved to the real aircraft electronic module. a) b) Fig. 3. Vision systems: a) night vision, and b) thermo-vision
Prototyping the Autonomous Flight Algorithms Using the Prepar3D® Simulator 223 4 Flying Model Description Flying object model should receive control data and returns the sensory variables. Another example of the Prepar3D® functionalities is reading the variables from the simulation environment. Being used by us data returned by the virtual sensors from the simulator are as follows: • LLA geodetic position: latitude (), longitude (), altitude above sea-level (), • object orientation: roll (rotation around the axis,α), pitch (rotation around the axis,β) and yaw (rotation around the axis,), • airspeed (. The third, and the mostly useful functionality, which is used, is controlling the aircraft. To control an object in the Prepar3D® environment algorithm uses four variables: rudder, elevator, ailerons and throttle. The main airplane axes and the three main control elements are illustrated in the fig. 4. Fig. 4. Airplane model with axes description 5 Controllers Cascade Object control and stabilization is based on PI controllers.[12] Algorithm uses the discrete PI controller, thatt is described by formula (1). ( 1) 2 where: – controller output at the moment , – error, controller input at the moment , – controller proportional gain, – controller integrator gain, – delay time.
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Studies in Computational Intelligen
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Aleksander Nawrat · Zygmunt Kuś E
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“In God We Trust, All others we o
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VIII Preface valuable information i
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Contents Part I: Design of Object D
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Contents XIII Technology Developmen
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XVI List of Contributors Adam Czorn
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XVIII List of Contributors Henryk M
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XX List of Contributors Józef Wron
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2 Design of Object Detection, Recog
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4 A. Babiarz et al. 1 Introduction
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Recognition and Location of Objects
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Omnidirectional Video Acquisition D
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Part III Design of Vision Based Con
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Vision System for Group of Mobile R
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A Distributed Control Group of Mobi
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344 Conclusions Control algorithms
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[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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