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This master’s thesis deals with the design and optimization of the quadrotor stabilization control. The basic physical properties and technical aspects of this specific device are illustrated using the NANOKOPTER platform. Dedicated mathematical model of quadrotor was implemented in MATLAB/Simulink environment to emulate the behavior and characteristics of real quadrotor. Based on this model a feasible method of stabilization control by means of state controller was conceived and developed. The proposed solution utilizing state controller was incorporated into the stabilization control procedures of the NANOKOPTER testing platform and its functionality successfully verified during field trials.

Currently, as one of the most popular technologies being discussed and experimented, the application of flying copters in different industries is facing an obvious barrier; which is how to avoid obstacles while flying. One of the industries among all is small-sized package delivery business, which is also the master topic of a series of experiments. The most popular designs that have used for the Flying Copter Obstacle Avoidance System such as lidar scanners and infrared rangefinders are significantly accurate. However, with the heavyweight, expensive price and higher power consumption, these systems cannot be put into mass production. To reduce the cost and power consumption of the Obstacle Avoidance System, an innovative vision-based low-cost Obstacle Distance Estimation System for flying copters is demonstrated in this thesis. The Fisheye Lens Camera is used to provide a broader detection range and accurate results. Compared to other standard vision-based systems, the Fish Lens Camera Distance Estimation System can provide (around 360 degrees) extensive view for obstacle detection. Through the parallax pictures captured by the camera and the trigonometric rules, the system can estimate the distance to the target obstacle with reasonable results.

Ziel dieser Arbeit ist es, ein System zur Lokalisierung für die Testumgebung des APOLI Projekts zu implementieren und zu validieren. Dabei soll mit Hilfe von Ultrabreitbandtechnologie ein lokales Echtzeitpositionsbestimmungssystem implementiert werden, welches die Funktionalität eines GPS-Empfängers emuliert. Dabei sollen die Positionsdaten an einen Copter übermittelt werden.

「自拍」是時下特有的社群文化，使用手持相機或自拍棒輔助拍照多有限制。然而搭載相機鏡頭的多軸飛行器（Camera-equipped drone）日漸普及，未來將有機會成為新的攝影器材。 本研究由初步訪談並分類「日常生活中操作相機」之心智模型：（1）以自身為中心，指揮攝影師移動之操控者模式（User Mode），適合近距操作，如自拍；（2）使用者透過相機視角，取得觀測角度之觀景窗模式（Viewfinder Mode），適合取得遠距影像，如災區探勘；（3）以第三人稱同時控制攝影者與被攝者的導演模式（Director Mode），擅於處理人與鏡頭相對空間關係，適合已預想好全局構圖與分鏡腳本。本研究依上述分類討論文獻與商品之優勢與限制，探索「操作空間中任務擺放之相機（多軸飛行器）」的理想互動方法後，將「操作空間中的相機」互動切分為兩階段流程設計：（1）Positioning階段：以User Mode直視飛行器，指揮相機位置；（2）Fine Tuning階段：以Viewfinder Mode透過觀景窗取得影像並微調拍攝參數。 本研究將著重於Positioning階段之飛行器控制互動方法，透過探討使用者的空間認知，提出三種符合使用者心智模型之飛行器運動座標系（球狀、柱狀與修正之三維座標），結合智慧型手機操作的類指向輸入方法（Semi-Direct Pointing），並實作沉浸式VR多軸飛行器體驗模擬器「Skyfie」，並進行兩次使用者測試，模擬使用者在3D空間中指揮飛行器移動至指定位置進行拍攝，實驗結果證明本研究提出之三種座標系統皆比傳統三維座標容易操作，並於最後討論各運動座標系適用之情境。 / Taking Selfie is a brand new type of photographic behavior and has become a phenomenon on social medias. In Asia, many people love taking and sharing selfies in their daily life and have create several tools to take good selfies suck as a selfie stick or a portable tripod. In the meantime, camera-equipped drones are getting more and more popular today. We can envision a future where personal flying selfie bots are always with us. Among the previous works and commercial products, the interaction techniques for controlling drone are mostly designed in drone-centric mode that require a longer training and are not easy for taking an anticipated shot. We investigate the user needs in taking selfies and propose an interaction technique that contains Positioning state and Fine Tuning state which are designed with user and viewfinder mode. Users can place a drone in a specified coordinate system by our semi-direct pointing technique and then compose the framing on the smartphone screen. We propose 3 coordinate systems for positioning stage and conduct two user studies in a simulated VR environment to validate the performance and user acceptances. In conclusion, we find that all three coordinate systems are more user friendly compare to traditional Cartesian coordinate system considering the test results and implementation cost.

A lightweight, powerful, yet efficient quad-rotor platform was designed and constructed to obtain experimental results of completely autonomous control of a hovering micro-UAV using a complete on-board vision system. The on-board vision and control system is composed of a Helios FPGA board, an Autonomous Vehicle Toolkit daughterboard, and a Kestrel Autopilot. The resulting platform is referred to as the Helio-copter. An efficient algorithm to detect, correlate, and track features in a scene and estimate attitude information was implemented with a combination of hardware and software on the FPGA, and real-time performance was obtained. The algorithms implemented include a Harris feature detector, template matching feature correlator, RANSAC similarity-constrained homography, color segmentation, radial distortion correction, and an extended Kalman filter with a standard-deviation outlier rejection technique (SORT). This implementation was designed specifically for use as an on-board vision solution in determining movement of small unmanned air vehicles that have size, weight, and power limitations. Experimental results show the Helio-copter capable of maintaining level, stable flight within a 6 foot by 6 foot area for over 40 seconds without human intervention.

This diploma thesis deals with processing of measured data from inertial navigation system in order these could be used for stabilization. There is general information about aerial vehicles called copters with emphasis on four-rotor construction called quadrocopter at first. Then mathematical model of quadrocopter in state space form is derived, the particular implementation of university developed quadrocopter is described and the design of data processing algorithm is presented with measured results. Finally achieved results are discussed.