Model Predictive Path Integral Methods with Reach-Avoid Tasks and Control Barrier Functions
<|reference_start|>Model Predictive Path Integral Methods with Reach-Avoid Tasks and Control Barrier Functions: 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.<|reference_end|>
@article{parwana2024model,
    title={Model Predictive Path Integral Methods with Reach-Avoid Tasks and
  Control Barrier Functions},
    author={Hardik Parwana, Mitchell Black, Georgios Fainekos, Bardh Hoxha, Hideki
  Okamoto, Danil Prokhorov},
    journal={arXiv preprint arXiv:2407.13693},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13693},
    primaryClass={cs.RO cs.SY eess.SY}
}