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.
翻译:最近的进展使得食品准备在高通量环境中实现了自动化,然而,成功部署这些机器人需要规划和实施快速、稳健和最终不发生碰撞的行为。在这项工作中,我们展示了利用控制障碍功能(CBFs)在非常详细和动态的碰撞环境中改变机器人操纵者以前产生的轨迹的新框架。这种方法动态地重新规划了在不断变化的环境中以前证实的行为,并且以高效的计算方式这样做。此外,这种方法为由此产生的轨迹提供了严格的安全保障,考虑到操纵者的真正基本动态。这种方法在现实世界的烹饪环境中对一个全面的机器人操纵者进行了广泛验证,并大大改善了计算时间和对再规划的稳健性。