案例研究：神经网络控制系统的运行时安全验证
Case Study: Runtime Safety Verification of Neural Network Controlled System
AI总结：
针对神经网络控制系统部署的安全隐患，本文以搭载激光雷达的Turtlebot导航系统为对象，用POLAR-Express开展运行时安全验证，提出安全在线控制器切换策略，经ROS2仿真验证了策略有效性及工具的能力与局限。
摘要：
神经网络正越来越多地应用于机器人、自动驾驶汽车等安全关键场景中。然而，神经网络控制系统（NNCSs）的部署引发了显著的安全隐患。近期的许多研究进展都忽略了实时场景下控制验证与安全保障的关键方面。本文开展了一项案例研究，采用前沿NNCS可达性分析工具POLAR-Express，对搭载激光雷达的Turtlebot导航系统进行运行时安全验证。该Turtlebot配备用于转向的神经网络控制器，在有障碍物的复杂环境中运行。我们开发了一种安全的在线控制器切换策略，可根据验证结果在原始NNCS控制器与避障控制器之间切换。我们在ROS2 Flatland仿真环境中开展了实验，探究了将POLAR-Express用于运行时验证的能力与局限性，并验证了我们提出的切换策略的有效性。
英文摘要：
Neural networks are increasingly used in safety-critical applications such as robotics and autonomous vehicles. However, the deployment of neural-network-controlled systems (NNCSs) raises significant safety concerns. Many recent advances overlook critical aspects of verifying control and ensuring safety in real-time scenarios. This paper presents a case study on using POLAR-Express, a state-of-the-art NNCS reachability analysis tool, for runtime safety verification in a Turtlebot navigation system using LiDAR. The Turtlebot, equipped with a neural network controller for steering, operates in a complex environment with obstacles. We developed a safe online controller switching strategy that switches between the original NNCS controller and an obstacle avoidance controller based on the verification results. Our experiments, conducted in a ROS2 Flatland simulation environment, explore the capabilities and limitations of using POLAR-Express for runtime verification and demonstrate the effectiveness of our switching strategy.
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。
