This paper introduces CBFKit, a Python/ROS toolbox for safe robotics planning and control under uncertainty. The toolbox provides a general framework for designing control barrier functions for mobility systems within both deterministic and stochastic environments. It can be connected to the ROS open-source robotics middleware, allowing for the setup of multi-robot applications, encoding of environments and maps, and integrations with predictive motion planning algorithms. Additionally, it offers multiple CBF variations and algorithms for robot control. The CBFKit is demonstrated on the Toyota Human Support Robot (HSR) in both simulation and in physical experiments.
@article{arxiv.2404.07158,
title = {CBFKIT: A Control Barrier Function Toolbox for Robotics Applications},
author = {Mitchell Black and Georgios Fainekos and Bardh Hoxha and Hideki Okamoto and Danil Prokhorov},
journal= {arXiv preprint arXiv:2404.07158},
year = {2024}
}