In the last years, the use of indoor drones has increased significantly in many different areas. However, one of the main limitations of the potential of these drones is the battery life. This is due to the fact that the battery size has to be limited since the drones have a maximum payload in order to be able to take-off and maintain the flight. Therefore, a recharging process need to be performed frequently, involving human intervention and thus limiting the drones applications. In order to solve this problem, this master thesis presents an autonomous recharging system for a nano drone, the Crazyflie 2.0 by Bitcraze AB. By automating the battery recharging process no human intervention will be needed, and thus overall mission time of the drone can be considerably increased, broadening the possible applications. The main goal of this thesis is the design and implementation of a control system for the indoor nano drone, in order to control it towards a landing platform and accurately land on it. The design and implementation of an actual recharging system is carried out too, so that in the end a complete full autonomous system exists. Before this controller and system are designed and presented, a research study is first carried out to obtain a background and analyze existing solutions for the autonomous landing problem. A camera is integrated together with the Crazyflie 2.0 to detect the landing station and control the drone with respect to this station position. A visual system is designed and implemented for detecting the landing station. For this purpose, a marker from the ArUco library is used to identify the station and estimate the distance to the marker and the camera orientation with respect to it. Finally, some tests are carried out to evaluate the system. The flight time obtained is 4.6 minutes and the landing performance (the rate of correct landings) is 80%. / Under de senaste åren har användningen av inomhusdrönare ökat betydligt på många olika områden. En av de största begränsningarna för dessa drönare är batteritiden. Detta beror på att batteristorleken måste begränsas eftersom drönarna har en väldigt begränsad maximal nyttolast för att kunna flyga. Därför måste de laddas ofta, vilket involverar mänskligt ingripande och därmed begränsar drönartillämpningarna. För att lösa detta problem presenterar detta examensarbete ett autonomt laddningssystem för en nanodrönare, Crazyflie 2.0. Genom att automatisera batteriladdningsprocessen behövs inget mänskligt ingrepp, och därigenom kan uppdragstiden för drönaren ökas avsevärt och bredda de möjliga tillämpningarna. Huvudmålet med denna avhandling är designen och implementationen av ett styrsystem för en inomhusdrönare, för att styra den mot en landningsplattform och landa korrekt på den. Arbetet inkluderar det faktiska laddningssystemet också, så att slutresultatet är ett fullständigt autonomt system. Innan regulatorn och systemet utformas och presenteras presenteras en genomgång av bakgrundsmaterial och analys av befintliga lösningar för problemet med autonom landning. En kamera monteras på Crazyflie 2.0 för att kunna detektera och positionera landningsstationen och styra drönaren med avseende på detta. För detektion används ArUcobibliotekets markörer vilka också gör det möjligt att räkna ut kamerans position och orientering med avseende på markören och därmed laddstationen. Slutligen utförs tester för att utvärdera systemet. Den erhållna flygtiden är 4,6 minuter och landningsprestandan (andel korrekta landningar på första försöket) är 80%.

Gesture Recognition is a new and upcoming trend that is being widely used in wearable devices and mobile handheld devices. The sensors like Accelerometer, Gyroscope, Heart rate monitor, Barometer and Ambient Light are mostly being included within the device to detect the static or continuous motion, rotational velocity, heartbeat rate of the user, pressure and light conditions for the device respectively. Implementing algorithms to capture the readings of these sensors and implementing them in a necessary way allows a user to use the wearable devices for a wide variety of applications. One such application is controlling Drone that takes user input to determine their motion. A Drone can accept signals from a combination of computer and a radio dongle and would fly according to the accepted commands. The wearable device can detect the motion of the wearer's hand when moved left, right, up, down etc using the Gyroscope sensor. This information can be used to process and send the signals to the Drone to enable wireless and gesture-based movement control.

A drone is an unmanned aerial vehicle with multiple forms of usage. Drones can be programmed to fly with different degrees of autonomous flight. Autonomous controlled flight makes it possible for the drone to fly without human involvement and it is then controlled solely by software. The goal of this project is to program the micro drone Crazyflie 2.1 to autonomously fly through an obstacle course in the shortest amount of time and a predetermined direction. The nature and placement of the obstacles are unknown beforehand. The obstacles are detected and avoided by using the obstacle detection sensor Multi- ranger. To achieve autonomous flight two possible navigation systems were tested, the Loco Positioning System and Flow deck. Flying the Crazyflie while using Flow deck as positioning system performed best, managing to fly through the obstacle course avoiding all obstacles.

The industry of UAVs has experienced a boost in recent years, and developments on both the hardware and algorithmic side have enabled smaller and more accessible drones with increased functionality. This thesis investigates the possibilities of autonomous target recognition and tracking in small, low-cost drones that are commercially available today. The design and deployment of an object recognition and tracking algorithm on a Crazyflie 2.1, a palm-sized quadcopter with a weight of a few tens of grams, is presented. The hardware is extended with an expansion board called the AI-deck featuring a fixed, front-facing camera and a GAP8 processor for machine learning inference. The aim is to create a vision-based autonomous control system for target recognition and following, with all computations being executed onboard and without any dependence on external input. A MobileNet-SSD object detector trained for detecting human bodies is used for detecting a person in images from the onboard camera. Proportional controllers are implemented for motion control of the Crazyflie, that process the output from the detection algorithm to move the drone to the desired position. The final implementation is tested indoors and proved to be able to detect a target and follow simple movements of a human moving in front of the drone. However, the reliability and speed of the detection need to be improved to achieve a satisfactory result.

I detta arbeta gjordes en undersökning om hur en drönare kan regleras vid olika viktbelastningar. Först gjordes en teoretisk undersökning av fyra olika reglermetoder, två modellbaserade och två icke-modellbaserade metoder. De två modellbaserade var linjär kvadratisk reglering och modell prediktiv kontroll. De två icke-modellbaserade var kaskad kontroll och PID regulator. Av alla metoderna valdes det att användas en PID regulator för regleringen. För att ställa in PID testades tre olika metoder, Ziegler-Nichols, lambda och AMIGO metoden. Alla dessa tre använde stegsvaret för systemet för att beräkna parametrarna till PID. Detta resulterade i flera olika parameterval för systemet. Parametervalen testades på drönaren och AMIGO metoden gav de bästa resultaten. De olika lasterna som testades var 4.7, 6.1 och 10.8 gram. AMIGO metoden kunde användas för att reglera drönaren upp till 6.1 gram därefter blev drönarens beteende allt för olinjärt. Sammanfattningsvis går det att använda en PID som ställs in med AMIGO metoden från stegsvaret för att reglera en drönare med mindre laster upp till ungefär sex gram. / This project tested how a drone can be controlled when loads are applied to it. First four different control methods were analyzed, two model based and two non-model based. The two model based were linear quadratic regulator and model predictive control. The two non-model based were cascade control and PID regulator. The PID regulator were chosen and three different methods to tune the PID was tested. Ziegler-Nichols, lambda and AMIGO method, all used the step response from the system to determine the parameters. These different methods gave different setup of parameters and the best result came from the AMIGO method. The different loads that were applied to the system was 4.7, 6.1 and 10.8 gram. The AMIGO method were able to tune the PID up to 6.1 grams, then the system lost to much of its linear behavior. To summarize the work a PID tuned by the AMIGO method using the step response were able to control a drone with a load up to about six grams.

This bachelors thesis presents a vision based control system for the quadrotor aerial vehicle,Crazy ie 2.0, developed by Bitcraze AB. The main goal of this thesis is to design andimplement an o-board control system based on visual input, in order to control the positionand orientation of the vehicle with respect to a single ducial marker. By integrating a cameraand wireless video transmitter onto the MAV platform, we are able to achieve autonomousnavigation and landing in relatively close proximity to the dedicated target location.The control system was developed in the programming language Python and all processing ofthe vision-data take place on an o-board computer. This thesis describes the methods usedfor developing and implementing the control system and a number of experiments have beencarried out in order to determine the performance of the overall vision control system. Withthe proposed method of using ducial markers for calculating the control demands for thequadrotor, we are able to achieve autonomous targeted landing within a radius of 10centimetres away from the target location. / I detta examensarbete presenteras ett visionsbaserat kontrollsystem for dronaren Crazy ie 2.0som har utvecklats av Bitcraze AB. Malet med detta arbete ar att utforma och implementeraett externt kontrollsystem baserat pa data som inhamtas av en kamera for att reglera fordonetsposition och riktning med avseende pa en markor placerad i synfaltet av kameran. Genom attintegrera kameran tillsammans med en tradlos videosandare pa plattformen, visar vi i dennaavhandling att det ar mojligt att astadkomma autonom navigering och landning i narheten avmarkoren.Kontrollsystemet utvecklades i programmeringsspraket Python och all processering avvisions-datan sker pa en extern dator. Metoderna som anvands for att utvecklakontrollsystemet och som beskrivs i denna rapport har testats under ett ertal experiment somvisar pa hur val systemet kan detektera markoren och hur val de olika ingaendekomponenterna samspelar for att kunna utfora den autonoma styrningen. Genom den metodsom presenteras i den har rapporten for att berakna styrsignalerna till dronaren med hjalp avvisuell data, visar vi att det ar mojligt att astadkomma autonom styrning och landning motmalet inom en radie av 10 centimeter.

This master thesis deals with the analysis of the current possibilities for object tracking in the image, based on which is designed a procedure for creating a system capable of tracking an object of interest. Part of this work is designing virtual reality for the needs of implementation of the tracking system, which is finally deployed and tested on a real prototype of unmanned vehicle.