  We propose a novel approach for sampling-based and control-based motion
planning that combines a representation of the environment obtained via a
modified version of optimal Rapidly-exploring Random Trees (RRT*), with
landmark-based output-feedback controllers obtained via Control Lyapunov
Functions, Control Barrier Functions, and robust Linear Programming. Our
solution inherits many benefits of RRT*-like algorithms, such as the ability to
implicitly handle arbitrarily complex obstacles, and asymptotic optimality.
Additionally, it extends planning beyond the discrete nominal paths, as
feedback controllers can correct deviations from such paths, and are robust to
discrepancies between the map used for planning and the real environment.
  We test our algorithms first in simulations and then in experiments, testing
the robustness of the approach to practical conditions, such as deformations of
the environment, mismatches in the dynamical model of the robot, and
measurements acquired with a camera with a limited field of view.
