Poppa, Florian2017-07-062017-07-06b43716015http://hdl.handle.net/1885/118906Scientists all over the world look for solutions to reduce greenhouse gas emissions in an effort to achieve proclaimed emissions reduction targets. An intriguing candidate with the potential to make a substantial contribution to this attempt is carbon capture and storage (CCS). The key advantage of CCS is that it provides the possibility to make a significant impact on the reduction of anthropogenic carbon dioxide (CO2) emissions from power plants and carbon-rich industry processes while maintaining existing fossil fuel energy infrastructure. The technique could therefore be used as a transitional solution until fossil fuels can be eliminated from the energy generation mix, and the energy efficiency of industrial processes as well as appliances and products is further improved. Like other technologies, CCS comes with its risks and rewards. To minimize possible negative impacts on humans as well as on the environment, it is necessary to understand the risks and to address them accordingly. A range of monitoring solutions for geological CO2 storage sites is available. However, a cost-effective solution for the regular observation of atmospheric CO2 concentrations (or tracer concentrations) of large areas above onshore geological CO2 storage sites has yet to be developed. This thesis discusses the use of a helicopter unmanned aerial vehicle (UAV) to fill this gap. The robot platform and its autopilot are designed to cope with ongoing sensor developments in addition to providing safety features necessary for the beyond line-of-sight operation of the UAV. The design focuses on the use of commercial off-the-shelf components for the aerial platform in order to shorten the development time and to reduce costs. The autopilot does neither enforce a specific helicopter model nor defines a set position estimation unit to be used. Access to the control loop enables low-level extensions like obstacle avoidance to be implemented. The developed solution allows the monitoring of an area of approximately 750m2 with one set of batteries in one altitude with a spatial resolution of 2m by 2m. Experiments show that point source leaks of as low as 100kg CO2 per day can be detected and their source located. As opposed to autonomous take-offs of the helicopter UAV, autonomous landings on small dedicated helipads require an accurate localization system. A time difference of arrival (TDOA) based acoustic localization system which is based on planar microphone arrays with at least four microphones is proposed. The system can be embedded into the landing platform and provides the accuracy necessary to land the UAV on a helipad of the size of 1m by 1m. A review of existing TDOA-based approaches is given. Simulations show that the developed approach outperforms its direct competitors for the targeted task. Furthermore, experimental results with the developed UAV confirm the feasibility of the introduced method. The effects of the sensor arrangement onto the quality of the calculated position estimates are also discussed. In order to combine robotic-assisted monitoring solutions and other monitoring strategies (e.g. sensor networks and individual sensors) into a single solution, it is necessary to have a framework which allows next to the measurement data analysis also the management (path changes, robot behavior changes, monitoring of internal robot state) of possibly multiple heterogeneous mobile robotic systems. A modular user interface (UI) framework is proposed which allows robots from different vendors and with various configurations next to individual sensors and sensor networks to be managed from a single application. The software system introduces a strict separation between the robot control software and UIs. UI implementations inside the UI framework can be reused across robot platforms, which can reduce the integration time of new robots significantly. The end user benefits by being able to manage a fleet of robots from various vendors and being able to analyze all the measurement data together in a single solution.en-AUUnmanned Aerial VehicleUAVEnvironmental Monitoring201710.25911/5d6e501753730