In this paper we propose a novel waypoint-based robot navigation method that combines reactive and deliberative actions. The approach uses reactive exploration to generate waypoints that can then be used by a deliberative system to plan future movements through the same environment. The waypoints are used largely to provide the interface between reactive and deliberative navigation and a range of methods could be used for either type of navigation. In the current work, an incremental decision tree method is used to navigate the robot reactively from the specified initial position to its destination avoiding obstacles in its path and a genetic algorithm method is used to perform the deliberative navigation. The new method is shown to have a number of practical advantages. Firstly, in contrast with many deliberative approaches, complete knowledge of the environment is not required, nor is it necessary to make assumptions regarding the geometry of obstacles. Secondly, the presence of a reactive navigator means it is always possible to continue directed movements in unknown or changing environments or when time constraints become particularly demanding. Thirdly, the use of waypoints allows escape from certain obstacle configurations that would normally trap robots navigated under the control of purely reactive methods. In addition, the results presented in this paper from a number of realistic simulated environments show that the adoption of waypoints significantly reduces the time to calculate a deliberative path.