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 NN 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.

翻译：本文针对具有避障需求的运动学自行车模型（KBM），提出了一种新颖的闭式控制屏障函数（CBF）及相应的控制器防护层。所提出的CBF与防护层——通过我们称为ShieldNN的算法设计——相较于现有方法具备两大关键优势。首先，ShieldNN直接在非仿射KBM动力学中考虑转向与速度约束；这与更通用的方法形成对比，后者通常仅考虑仿射动力学且无法保证控制约束下的不变性。其次，ShieldNN为每个状态提供闭式安全控制集合，而通用方法通常依赖优化算法为每个状态生成单一瞬时解。这些优势共同使得ShieldNN特别适合作为强化学习（RL）神经网络控制器训练期间的高效多障碍物安全动作（即多屏障函数防护）机制。实验表明，在多障碍物场景下，ShieldNN能显著提升RL训练回合的完成率，从而验证了ShieldNN在训练基于RL的控制器中的重要价值。