  Autonomy advances have enabled robots in diverse environments and close human
interaction, necessitating controllers with formal safety guarantees. This
paper introduces an experimental platform designed for the validation and
demonstration of a novel class of Control Barrier Functions (CBFs) tailored for
Unmanned Ground Vehicles (UGVs) to proactively prevent collisions with
kinematic obstacles by integrating the concept of collision cones. While
existing CBF formulations excel with static obstacles, extensions to
torque/acceleration-controlled unicycle and bicycle models have seen limited
success. Conventional CBF applications in nonholonomic UGV models have
demonstrated control conservatism, particularly in scenarios where
steering/thrust control was deemed infeasible. Drawing inspiration from
collision cones in path planning, we present a pioneering CBF formulation
ensuring theoretical safety guarantees for both unicycle and bicycle models.
The core premise revolves around aligning the obstacle's velocity away from the
vehicle, establishing a constraint to perpetually avoid vectors directed
towards it. This control methodology is rigorously validated through
simulations and experimental verification on the Copernicus mobile robot
(Unicycle Model) and FOCAS-Car (Bicycle Model).
