Traditional AI methods for navigational planning use qualitative spatial representations and reasoning. Traditional robotics techniques for this task are based on numerical representations and reasoning. Recent work on robotics posits mechanisms for reactive control that directly map perceptions of the world to actions on it. This in turn has given rise to hybrid robot architectures that combine navigational planning and reactive control. But following traditional robotics techniques, navigational planning in these hybrid arclfitectures to() uses numerical methods. This raises the following question: Given a hybrid robot architecture, are numerical methods really needed for navigational planning? To explore this issue, we integrated a multistrategy qualitative navigational planner with a reactive-control mechanism. Then we embodied the integrated system on a physical robot. Next we gave the robot a series of navigation tasks in a visually structured spatial world containing discrete pathways, and monitored its actions as it executed the tasks in the presence of both static and moving obstacles. Our experiments show that for hybrid robots qualitative methods are sufficient for navigational planning in at least one class of spatial worlds.