As a student project at Halmstad University, CatFish aims to make water quality measurement easier by the development of a set of cooperating measurement drones in and under water and up in the air. To minimize human effort during the operation of the CatFish system, the drones shall act autonomously to reach given sets of target coordinates on their own to fulfill desired measurement tasks on arrival. This thesis presents the development of an autonomy system for the water surface drone from development, integration and testing in a prototype in different configurations and under continuous improvement with the goal to identify and implement an ideal system solution. In addition to this basic autonomy system, the second major part of the presented work consists of the development of an object detection and tracking system for increased safety of the floating drone and its environment. Confronted with very limited computational power which is a property of most embedded systems, this thesis puts a strong focus on the investigation of state-of-the-art technology in the area of computer vision to identify the most promising approaches in the constraint environment.

To achieve the goal of creating a useful solution, the development of different object detection and object tracking algorithms and combinations of both methods and their evaluation was done with a concentration on algorithm speed rather then accuracy in the first place. The outcome is a first set of possible implementations of object detection and tracking models, accompanied with recommendations for further speed improvements, to create an obstacle avoidance system that can extend the developed autonomy system of the water surface drone of the CatFish project in the future.