  The rapid advancement of robotics necessitates robust tools for developing
and testing safe control architectures in dynamic and uncertain environments.
Ensuring safety and reliability in robotics, especially in safety-critical
applications, is crucial, driving substantial industrial and academic efforts.
In this context, we extend CBFkit, a Python/ROS2 toolbox, which now
incorporates a planner using reach-avoid specifications as a cost function.
This integration with the Model Predictive Path Integral (MPPI) controllers
enables the toolbox to satisfy complex tasks while ensuring formal safety
guarantees under various sources of uncertainty using Control Barrier Functions
(CBFs). CBFkit is optimized for speed using JAX for automatic differentiation
and jaxopt for quadratic program solving. The toolbox supports various robotic
applications, including autonomous navigation, human-robot interaction, and
multi-robot coordination. The toolbox also offers a comprehensive library of
planner, controller, sensor, and estimator implementations. Through a series of
examples, we demonstrate the enhanced capabilities of CBFkit in different
robotic scenarios.
