In this paper, we describe a planning and control approach in terms of sampling using Rapidly-exploring Random Trees (RRTs), which were introduced in [7, 9]. We review RRTs for motion planning and show how to use them to solve standard nonlinear control problems. We extend them to the case of hybrid systems and describe our modifications to LaValle’s Motion Strategy Library to allow for hybrid motion planning. Finally, we extend them to purely discrete spaces (using heuristic evaluation as a distance metric) and provide computational experiments comparing them to conventional methods, such as A*.