Synthesis of Control Barrier Functions Using a Supervised Machine Learning Approach
Synthesis of Control Barrier Functions Using a Supervised Machine Learning Approach
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DOI：
10.1109/iros45743.2020.9341190
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Mohit Srinivasan;A. Dabholkar;S. Coogan;P. Vela
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文献类型：
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作者：
Mohit Srinivasan;A. Dabholkar;S. Coogan;P. Vela
控制障碍函数是用于保证机器人系统安全的数学构造。当集成为二次规划优化问题中的约束时，可以实现具有实时性能要求的瞬时控制综合，用于机器人应用。目前的使用假设安全屏障功能的充分知识，但在某些情况下，安全区域必须从传感器测量在线估计。在这些情况下，必须在线合成相应的屏障功能。本文介绍了一种学习框架，估计控制障碍功能的传感器数据。这样做提供了在未知状态空间区域中的系统操作，而不损害安全性。这里，支持向量机分类器提供由从传感器测量获得的安全和不安全状态的集合确定的屏障函数规范。提供了理论上的安全保障。配备激光雷达的全向机器人的实验ROS为基础的仿真结果证明安全操作。
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.