This paper presents a framework for the safety-critical control of robotic systems, when safety is defined on safe regions in the configuration space. To maintain safety, we synthesize a safe velocity based on control barrier function theory without relying on a -- potentially complicated -- high-fidelity dynamical model of the robot. Then, we track the safe velocity with a tracking controller. This culminates in model-free safety critical control. We prove theoretical safety guarantees for the proposed method. Finally, we demonstrate that this approach is application-agnostic. We execute an obstacle avoidance task with a Segway in high-fidelity simulation, as well as with a Drone and a Quadruped in hardware experiments.
翻译:本文为在配置空间安全区域确定安全时对机器人系统进行安全临界控制提供了一个框架。 为了维护安全,我们根据控制屏障功能理论合成了安全速度,而没有依赖机器人的 -- -- 潜在复杂的 -- -- 高度忠诚动态模型。然后,我们用一个追踪控制器跟踪安全速度。这最终导致无模型安全临界控制。我们证明对拟议方法的理论安全保障。最后,我们证明这一方法是应用敏感。我们用高不忠模拟的Segway执行避免障碍的任务,以及一个无人机和硬件实验的四重置。