This report describes a docking system to allow autonomous battery charging of a mobile robot, and a Web interface that allows long-term unaided use of a sophisticated mobile robot by untrained Web users around the world. The docking system and Web interface are applied to the biologically inspired RatSLAM system as a foundation for testing both its long-term stability and its practicality for real-world applications. While there are existing battery charging and Web interface systems for mobile robots, the developed solution combines the two, resulting in a self-sufficient robot that can recharge its own batteries and stay accessible from the Web. Existing mobile robots on the Internet require manual charging by a human operator, leading to significant periods when the robot is offline. Furthermore, since the robot may be operational for extended periods without powering down, it may perform learning operations that require significantly longer operation than a single battery-recharge cycle would allow. The implemented Web interface makes use of the RatSLAM navigation system. RatSLAM provides the onboard intelligence for the robot to navigate to the user-supplied goal locations (such as “go to location X”), despite long paths or obstacles in the environment. The majority of the existing robot interfaces on the Internet provide direct control of the robot (such as “drive forward”) and therefore the users suffer greatly from the inherent delays of the Internet due to the time lag between performing an action and seeing the feedback. Instead, the robot in this study uses an onboard intelligent navigation system to generate all low-level commands. Due to the minimal input required to give high-level commands to the robot, the system is robust to the long and highly unpredictable delays of Internet communication. Traditional methods of autonomous battery charging for mobile robots have had limited reliability, often due to the mechanical docking system requiring a highly precise connection. Therefore, the mechanical design of the implemented battery charging system improves reliability by allowing for a significantly larger navigation error. In addition, the robot uses a standard vision sensor for both the long-range and short-range stages of navigation to the battery charger, compared to the many systems that require an omnidirectional camera and a high-resolution Laser range finder for this process. The result of this study is a public web interface at "http://ratslam.itee.uq.edu.au/robot.html" (currently offline), where any Web user in the world can watch and control the live mobile robot that is using RatSLAM for navigation, as it drives in its laboratory environment without human assistance.

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%.