理解安全关键型UAV软件中的边界函数
Understanding Bounding Functions in Safety-Critical UAV Software
摘要：
无人机（UAV）是一种新兴的计算平台，以其安全关键需求而闻名。在本文中，我们进行了一项实证研究，广泛使用的开源无人机软件框架，狗仔队，从自下而上的开发人员在该领域的角度来理解无人机软件的安全关键问题的目标。我们将重点放在边界函数（Bounding Functions，BF）的使用上，这是狗仔队开发人员在变量范围内注入的运行时检查。通过对Paparazzi自动驾驶仪软件中的BF进行深入分析，我们发现其中大量（109个实例）用于绑定对无人机的网络物理性质至关重要的安全关键变量，例如其推力，速度和传感器值。这项研究的新贡献有两个方面。首先，我们采取一种静态的方法来分类所有BF实例，提出了一种新的基于数据类型的5类分类法，具有细粒度的洞察力的BF在确保无人机系统的安全性的作用。其次，我们动态评估的影响BF使用通过差分方法，建立与无BF的无人机行为差异。静态和动态双管齐下的方法共同阐明了安全关键型无人机软件系统的很少研究的设计空间。
英文摘要：
Unmanned Aerial Vehicles (UAVs) are an emerging computation platform known for their safety-critical need. In this paper, we conduct an empirical study on a widely used open-source UAV software framework, Paparazzi, with the goal of understanding the safety-critical concerns of UAV software from a bottom-up developer-in-the-field perspective. We set our focus on the use of Bounding Functions (BFs), the runtime checks injected by Paparazzi developers on the range of variables. Through an in-depth analysis on BFs in the Paparazzi autopilot software, we found a large number of them (109 instances) are used to bound safety-critical variables essential to the cyber-physical nature of the UAV, such as its thrust, its speed, and its sensor values. The novel contributions of this study are two fold. First, we take a static approach to classify all BF instances, presenting a novel datatype-based 5-category taxonomy with fine-grained insight on the role of BFs in ensuring the safety of UAV systems. Second, we dynamically evaluate the impact of the BF uses through a differential approach, establishing the UAV behavioral difference with and without BFs. The two-pronged static and dynamic approach together illuminates a rarely studied design space of safety-critical UAV software systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
