We study the problem of designing dynamically feasible trajectories and controllers that drive a quadrotor to a desired state in state space. We focus on the development of a family of trajectories defined as a sequence of segments, each with a controller parameterized by a goal state. Each controller is developed from the dynamic model of the robot and then iteratively refined through successive experimental trials to account for errors in the dynamic model and noise in the actuators and sensors. We show that this approach permits the development of trajectories and controllers enabling aggressive maneuvers such as flying through narrow, vertical gaps and perching on inverted surfaces with high precision and repeatability.
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