Recent advances allow for the automation of food preparation in high-throughput environments, yet the successful deployment of these robots requires the planning and execution of quick, robust, and ultimately collision-free behaviors. In this work, we showcase a novel framework for modifying previously generated trajectories of robotic manipulators in highly detailed and dynamic collision environments using Control Barrier Functions (CBFs). This method dynamically re-plans previously validated behaviors in the presence of changing environments -- and does so in a computationally efficient manner. Moreover, the approach provides rigorous safety guarantees of the resulting trajectories, factoring in the true underlying dynamics of the manipulator. This methodology is extensively validated on a full-scale robotic manipulator in a real-world cooking environment, and has resulted in substantial improvements in computation time and robustness over re-planning.
@article{arxiv.2205.01026,
title = {Safety-Critical Manipulation for Collision-Free Food Preparation},
author = {Andrew Singletary and William Guffey and Tamas G. Molnar and Ryan Sinnet and Aaron D. Ames},
journal= {arXiv preprint arXiv:2205.01026},
year = {2022}
}