论文标题
控制屏障功能和人造潜在领域的比较分析避免障碍物
Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidance
论文作者
论文摘要
人造潜在领域（APF）及其变体已成为避免移动机器人和操纵器的碰撞的主要内容。多年来，它与模型无关的性质，易于实施和实时性能在其持续的成功中发挥了重要作用。另一方面，控制屏障功能（CBF）是一种最新的开发，通常用于以标称控制器上的过滤器形式实时确保非线性系统的安全性。在本文中，我们解决了APFS和CBF之间的连接。在理论层面上，我们证明APF是CBFS的特殊情况：给定APF的一个人获得了CBF，而相反的情况不正确。此外，我们证明从APF获得的CBF具有其他有益特性，可以应用于非线性系统。实际上，我们在简单的说明性示例中避免障碍物和四型四极管的情况下，在模拟和硬件上使用板载传感来比较APF和CBF的性能。这些比较表明CBF的表现要优于APF。
Artificial potential fields (APFs) and their variants have been a staple for collision avoidance of mobile robots and manipulators for almost 40 years. Its model-independent nature, ease of implementation, and real-time performance have played a large role in its continued success over the years. Control barrier functions (CBFs), on the other hand, are a more recent development, commonly used to guarantee safety for nonlinear systems in real-time in the form of a filter on a nominal controller. In this paper, we address the connections between APFs and CBFs. At a theoretic level, we prove that APFs are a special case of CBFs: given a APF one obtains a CBFs, while the converse is not true. Additionally, we prove that CBFs obtained from APFs have additional beneficial properties and can be applied to nonlinear systems. Practically, we compare the performance of APFs and CBFs in the context of obstacle avoidance on simple illustrative examples and for a quadrotor, both in simulation and on hardware using onboard sensing. These comparisons demonstrate that CBFs outperform APFs.
