用学习方法改进车辆避障保守性，更精准利用形状与朝向信息
A Learning-Based Control Barrier Function for Car-Like Robots: Toward Less Conservative Collision Avoidance
- 基于神经网络学习非光滑安全裕度，融合车辆朝向与真实形状
- 实测绕行时横向空间需求减少33.5%，计算开销几乎不变
- 适合需要精细避障的自动驾驶或移动机器人场景
我们提出一种基于学习的控制屏障函数（CBF），以降低类汽车机器人在密集环境中的避障保守性。传统CBF通常使用机器人中心间的欧氏距离作为安全裕度，忽略其朝向并近似为圆形，虽满足平滑性和可微性要求，但在复杂场景中可能导致过度保守行为。为此，我们设计了一种同时考虑机器人朝向和实际几何形状的安全裕度，实现更精确的安全区域估计。由于该裕度不可微，我们采用神经网络进行近似以保证可微性。此外，我们提出相对动力学概念，使学习过程可处理。在基于非线性运动学自行车模型的案例研究中建立了理论基础。数值实验显示，在超车与绕行场景中，本方法可减少33.5%的横向空间需求，且额外计算时间可忽略不计。
原文摘要 · Abstract (English)
We propose a learning-based Control Barrier Function (CBF) to reduce conservatism in collision avoidance for car-like robots. Traditional CBFs often use the Euclidean distance between robots' centers as a safety margin, which neglects their headings and approximates their geometries as circles. Although this simplification meets the smoothness and differentiability requirements of CBFs, it may result in overly conservative behavior in dense environments. We address this by designing a safety margin that considers both the robot's heading and actual shape, thereby enabling a more precise estimation of safe regions. Because this safety margin is non-differentiable, we approximate it with a neural network to ensure differentiability. In addition, we propose a notion of relative dynamics that makes the learning process tractable. In a case study, we establish the theoretical foundation for applying this notion to a nonlinear kinematic bicycle model. Numerical experiments in overtaking and bypassing scenarios show that our approach reduces conservatism (e.g., requiring 33.5% less lateral space for bypassing) without incurring significant extra computation time. Code: https://github.com/bassamlab/sigmarl
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