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

Robust Sample-Based Output-Feedback Path Planning