ShieldNN：面向不安全神经网络控制器的可证明安全神经网络滤波器
ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers
AI总结：
本文提出ShieldNN闭式CBF防护算法，面向非仿射KBM直接处理转向与速度约束并给出安全控制集合，可在RL神经网络控制器训练中实现高效多障碍物防护，显著提升训练回合完成率。
摘要：
本文针对运动学自行车模型（Kinematic Bicycle Model，KBM）的避障问题，提出了一种新颖的闭式控制屏障函数（Control Barrier Function，CBF）及相应的控制器防护机制。所提出的CBF与防护机制由我们称为ShieldNN的算法设计，相较现有方法具有两个关键优势。首先，ShieldNN直接在非仿射KBM动力学中考虑转向和速度约束；这与更通用的方法形成对比，后者通常只考虑仿射动力学，且无法保证控制约束下的不变性。其次，ShieldNN为每个状态提供一组闭式安全控制，而更通用的方法通常依赖优化算法为每个状态生成单一瞬时控制。这些优势共同使ShieldNN特别适合在基于强化学习（Reinforcement Learning，RL）的神经网络控制器训练期间，作为高效的多障碍物安全动作机制（即多屏障函数防护）。实验表明，在存在多个障碍物时，ShieldNN显著提高了RL训练回合的完成率，从而证明了ShieldNN在训练基于RL的控制器方面的价值。
英文摘要：
In this paper, we develop a novel closed-form Control Barrier Function (CBF) and associated controller shield for the Kinematic Bicycle Model (KBM) with respect to obstacle avoidance. The proposed CBF and shield -- designed by an algorithm we call ShieldNN -- provide two crucial advantages over existing methodologies. First, ShieldNN considers steering and velocity constraints directly with the non-affine KBM dynamics; this is in contrast to more general methods, which typically consider only affine dynamics and do not guarantee invariance properties under control constraints. Second, ShieldNN provides a closed-form set of safe controls for each state unlike more general methods, which typically rely on optimization algorithms to generate a single instantaneous for each state. Together, these advantages make ShieldNN uniquely suited as an efficient Multi-Obstacle Safe Actions (i.e. multiple-barrier-function shielding) during training time of a Reinforcement Learning (RL) enabled Neural Network controller. We show via experiments that ShieldNN dramatically increases the completion rate of RL training episodes in the presence of multiple obstacles, thus establishing the value of ShieldNN in training RL-based controllers.
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