  Autonomous robot navigation can be particularly demanding, especially when
the surrounding environment is not known and safety of the robot is crucial.
This work relates to the synthesis of Control Barrier Functions (CBFs) through
data for safe navigation in unknown environments. A novel methodology to
jointly learn CBFs and corresponding safe controllers, in simulation, inspired
by the State Dependent Riccati Equation (SDRE) is proposed. The CBF is used to
obtain admissible commands from any nominal, possibly unsafe controller. An
approach to apply the CBF inside a safety filter without the need for a
consistent map or position estimate is developed. Subsequently, the resulting
reactive safety filter is deployed on a multirotor platform integrating a LiDAR
sensor both in simulation and real-world experiments.
