论文标题
使用监督的机器学习方法综合控制屏障功能
Synthesis of Control Barrier Functions Using a Supervised Machine Learning Approach
论文作者
论文摘要
控制障碍功能是用于保证机器人系统安全性的数学构造。当将作为二次编程优化问题中的约束集成在一起时，对于机器人应用程序，可以实现具有实时性能需求的瞬时控制合成。普遍的用途已经完全了解了安全性屏障功能，但是在某些情况下，必须通过传感器测量在线估算安全区域。在这些情况下，必须在线合成相应的屏障功能。本文介绍了一个学习框架，用于从传感器数据估算控制屏障功能。这样做可以在未知的状态空间区域内进行系统操作，而不会损害安全性。在这里，支持向量机分类器提供了由从传感器测量中获得的安全和不安全状态确定的屏障功能规范。提供了理论上的安全保证。配备LIDAR的全向机器人基于实验ROS的仿真结果表明安全操作。
Control barrier functions are mathematical constructs used to guarantee safety for robotic systems. When integrated as constraints in a quadratic programming optimization problem, instantaneous control synthesis with real-time performance demands can be achieved for robotics applications. Prevailing use has assumed full knowledge of the safety barrier functions, however there are cases where the safe regions must be estimated online from sensor measurements. In these cases, the corresponding barrier function must be synthesized online. This paper describes a learning framework for estimating control barrier functions from sensor data. Doing so affords system operation in unknown state space regions without compromising safety. Here, a support vector machine classifier provides the barrier function specification as determined by sets of safe and unsafe states obtained from sensor measurements. Theoretical safety guarantees are provided. Experimental ROS-based simulation results for an omnidirectional robot equipped with LiDAR demonstrate safe operation.
