Small fixed-wing unmanned aerial vehicles (UAVs) are becoming increasingly capable of flying at low altitudes and in constrained environments. This paper addresses the problem of automating the flight of a fixed-wing UAV through highly constrained environments. The main contribution of this paper is the development of a maneuver space, integrating steady and transient agile maneuvers for a class of fixed-wing aircraft. The maneuver space is integrated into the rapidly-exploring random trees (RRT) algorithm. The RRT-based motion planner, together with a flight control system, is demonstrated in simulations and flight tests to efficiently generate and execute a motion plan through highly constrained 3D environments in real-time. The flight experiments—which effectively demonstrated the usage of three highly agile maneuvers—were conducted using only on-board sensing and computing.