  Control Barrier Functions (CBF) are widely used to enforce the
safety-critical constraints on nonlinear systems. Recently, these functions are
being incorporated into a path planning framework to design safety-critical
path planners. However, these methods fall short of providing a realistic path
considering both the algorithm's run-time complexity and enforcement of the
safety-critical constraints. This paper proposes a novel motion planning
approach using the well-known Rapidly Exploring Random Trees (RRT) algorithm
that enforces both CBF and the robot Kinodynamic constraints to generate a
safety-critical path. The proposed algorithm also outputs the corresponding
control signals that resulted in the obstacle-free path. The approach also
allows considering model uncertainties by incorporating the robust CBF
constraints into the proposed framework. Thus, the resulting path is free of
any obstacles and accounts for the model uncertainty from robot dynamics and
perception. Result analysis indicates that the proposed method outperforms
various conventional RRT-based path planners, guaranteeing a safety-critical
path with minimal computational overhead. We present numerical validation of
the algorithm on the Hamster V7 robot car, a micro autonomous Unmanned Ground
Vehicle that performs dynamic navigation on an obstacle-ridden path with
various uncertainties in perception noises and robot dynamics.
