This report is about the implementation of autonomy and control on a water quality measuring unmanned surface vehicle. The project was termed Catfish and involved five teams focusing on different aspects of the initial goal to create an autonomous three-part system; a surface drone, a submerged drone and flying drone. In this iteration of the Catfish project the focus laid on creating the surface drone and submerged drone as the Catfish project will improve over generations of thesis projects. The author of the report was in the Control and Autonomy team and had been tasked with giving the surface drone the autonomy needed to make this project viable. Existing advances made in autonomy was adopted and tested. With the help of estimation algorithms, and sensor fusion, a flight controller navigates the surface drone between a set of GPS waypoints. It is also able to counteract the external forces wind, waves and stream to keep its position. To reach this autonomy four test phases were conducted on a pre-prototype with progressively increased difficult autonomy starting with manual control and ending in advanced autonomy. When the advanced missions were executed the speed and accuracy of two different thruster configurations were examined and the best performing out of the two was implemented on the final prototype the other teams had designed. The project ended with a fully autonomous system that was able to execute all the navigational maneuvers required to operate autonomous water quality measuring missions. / Den här rapporten handlar om implementationen av autonomi och kontroll på en vattenkvalitetsmätande vattenburen drönare. Projektet fick namnet Catfish och blev indelat i fem teams som fokuserade på olika aspekter av ett 3-delsystem; en vattenburen, en undervattens och en flygande drönare. I denna iteration av Catfish projektet fokuserade medlemmarna på att utveckla den vattenburna och undervattens drönaren då projektet kommer fortsätta utvecklas under kommande generationer av Catfish projektrapporter. Författaren av den här rapporten ingick i ett team som hette "Control and Autonomy" och hade i uppgift att installera en autonom intelligens till den vattenburna drönaren för att göra Catfish prototypen användbar. Befintliga framsteg inom forskningsområdet blev granskade och testade. Genom att använda uppskattningsalgoritmer och "sensor fusion" lyckades en "flight controller" navigera drönaren mellan GPS waypoints och även behålla sin position genom att motverka krafterna från vind, vågor och strömmar. För att uppnå denna nivå av autonomi utför en förprototyp fyra test faser av ökad autonomisk svårhetsgrad. Under uppdraget blev hastigheten och precisionen av två olika motoruppsättningar undersöka och den som presterade bäst blev implementerad på den slutgiltig designen som de andra teamen hade utvecklat. Projektet avslutades med att ett fullt autonomt system blev utvecklat som var kapabel till att utföra alla navigationsmanövrar nödvändiga för att genomföra autonoma vattenkvalitetsmätningsuppdrag.

The aim of this thesis is to modify commercially produced drone DJI Matrice 100 and replace its original control unit by open source Pixhawk and its accessories. Subsequently, it deals with the selection of suitable open source firmware for Pixhawk and its configuration on the device. Another part is dedicated to the possibilities of using the Robotic Operating System (ROS) and its Mavros libraries on the onboard computer Raspberry Pi. By using Mavros, it examines the possibilities of drone flight control, both in the simulation environment and in the real environment.

This master‘s thesis deals with development and realisation of robotic lawn mower with satelite navigation. It begins with preparation of a platform for outdoor mobile robot navigation and it’s control HW and SW. There are discussed different options of navigation both commercial and experimental. Further on I have chosen the right GNSS receiver based on market research and user experience. The GNSS receiver’s parameters are measured with different antennas. Following with the choice of suitable open-source control unit and it’s software implementation. Furthermore control from a companion computer is described and physical realisation is done. In the end of the thesis activation of the whole mower is performed and described. Lastly there are discussed possible ways of future development.

In this paper is described creation of a robot in Robot Operating system (ROS) withAckermann steering. It contains the principle of Ackermann steering geometry, search ofcontroller boards and basics of ROS structure. A RC car with connected PixHawk controlleris used as a basis of the robot. On the robot is placed an onboard computer Raspberry Pi3 with running ROS. This computer is connected to a laptop through Wi-Fi network. Theprocedure of starting up the robot and ROS is also described in this paper, as well asdesign of the graphical user interface (GUI) that will display sensory data and allow otherfunctionality. Another part of thesis explains principle of an optical encoder and how tocreate your own encoder which can detect rotation of a wheel. This is used to implementrobot odometry. The structure of ROS navigation library is analyzed with regards to itscommissioning. Implementation of the GUI and navigation library will follow in the masterthesis.

<p> This thesis presents work and simulations containing the use of Artificial Intelligence for real-time perception and real-time anomaly detection using the computer and sensors onboard an Unmanned Aerial Vehicle. One goal of this research is to develop a highly accurate, high-performance computer vision system that can then be used as a framework for object detection, obstacle avoidance, motion estimation, 3D reconstruction, and vision-based GPS denied path planning. The method developed and presented in this paper integrates software and hardware techniques to reach optimal performance for real-time operations. </p> <p>This thesis also presents a solution to real-time anomaly detection using neural networks to further the safety and reliability of operations for the UAV. Real-time telemetry data from different sensors are used to predict failures before they occur. Both these systems together form the framework behind the Intelligent UAV platform, which can be rapidly adopted for different varieties of use cases because of its modular nature and on-board suite of sensors. </p>