A control architecture is a key component in the development of an autonomous robot. The control architecture has the goal of accomplishing a mission which can be divided into a set of sequential tasks. This chapter has presented behaviour-based control architectures as a methodology to implement this kind of controller. Its high interaction with the environment, as well as its fast execution and reactivity, are the keys to its success in controlling autonomous robots. The main attention has been given to the coordination methodology. Competitive coordinators assure the robustness of the controller, whereas cooperative coordinators determine the performance of the final robot trajectory. The structure of a control architecture for an autonomous robot has been presented. Two main layers are found in this schema; the deliberative layer which divides the robot mission into a set of tasks, and the behaviour-based layer which is in charge of accomplishing these tasks. This chapter has focused only on the behaviour-based layer. A behaviour coordination approach has been proposed, its main feature being a hybrid coordination of behaviours between competitive and cooperative approaches. A second distinctive part is the use of a learning algorithm to learn the internal mapping between the environment state and the robot actions. Then, the chapter has introduced the main features of the URIS AUV and its experimental set-up. According to the navigation system we have shown that the estimation of the robot position and velocity can be effectively performed with a vision-based system using mosaicking techniques. This approach is feasible when the vehicle navigates near the ocean floor and is also useful to generate a map of the area. Finally, results on real data have been shown through an example in which the behaviour-based layer controlled URIS in a target following task. In these experiments several behaviours were in charge of the control of the robot to avoid obstacles, to find the target and to follow it. One of these behaviours was automatically learnt demonstrating the feasibility of the learning algorithms. Finally, we have also shown the performance of the navigation system using the visual mosaicking techniques.