Quadrotor UAV's have become extremely popular over the last decade, as they combine great agility with mechanic simplicity. For tasks such as area surveillance or maintaining a communication network, the deployment of multi-agent systems is required. In these scenarios, it often is necessary for the robot teams to navigate in formation while avoiding static and dynamic obstacles. In situations, where team keeping is not crucial it can be beneficial to split and merge robot teams. This could be the case, if multiple targets are supposed to be tracked, or due to obstacle avoidance in order to improve goal progress and help avoiding deadlocks.

Within this thesis a novel approach for splitting and merging of robot teams moving in formation is developed. The method extends an existing geometric method for local formation control, which uses the approximation of typically non-convex workspaces by obstacle free convex regions. The regions are used to constrain a non-linear cost function, which is minimized through Sequential Convex Programming (SCP). This determines the references position for the robots and furthermore allows for the adjustment of the formation shape according to the environment. This thesis presents a goal directed version of the obstacle-free convex regions. These regions are used to determine team splitting and merging based on region intersections. The method takes into account limited sensing and communication range of the agents. Furthermore computations are done in a distributed manner to allow for better scaling with the number of team members.

The presented method is validated through simulations in various scenarios. Furthermore, an experimental framework is recreated and experiments with up to four quadrotor UAVs of the type Parrot Bebop 2 are conducted. The approach shows sufficient real-time capabilities in static and dynamic environments, while maintaining safe navigation of the drones.