Case Study: Safety Verification of an Unmanned Underwater Vehicle
Case Study: Safety Verification of an Unmanned Underwater Vehicle
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案例研究：无人水下航行器的安全验证
DOI：
10.1109/spw50608.2020.00047
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Taylor T. Jonhson
中科院分区：
文献类型：
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作者：
Diego Manzanas Lopez;Patrick Musau;Nathaniel P. Hamilton;Hoang;Taylor T. Jonhson
这篇手稿评估了一种神经网络控制器的安全性，该控制器寻求确保无人水下机器人(UUV)不会与其路径上的静态物体相撞。为了实现这一点，我们使用了一些方法，可以通过使用星集来确定UUV所有组件的精确输出可达集合。星集是一种计算上有效的集合表示，擅长刻画大的输入空间。它支持廉价高效的仿射映射运算和与半空间的交集的计算。本文所讨论的系统是一个比以往工作中考虑的神经网络控制系统(NNCS)更复杂的系统，它总共由四个组成部分组成。我们的实验评估使用了四种不同的场景，表明我们的基于星集的方法是可扩展的，并且可以有效地用于分析真实世界的网络物理系统(CPS)的安全性。
This manuscript evaluates the safety of a neural network controller that seeks to ensure that an Unmanned Underwater Vehicle (UUV) does not collide with a static object in its path. To achieve this, we utilize methods that can determine the exact output reachable set of all the UUV's components through the use of star-sets. The star-set is a computationally efficient set representation adept at characterizing large input spaces. It supports cheap and efficient computation of affine mapping operations and intersections with half-spaces. The system under consideration in this work represents a more complex system than Neural Network Control Systems (NNCS) previously considered in other works, and consists of a total of four components. Our experimental evaluation uses four different scenarios to show that our star-set based methods are scalable and can be efficiently used to analyze the safety of real-world cyber-physical systems (CPS).
DOI：
10.1145/3302504.3311802
发表时间：
2018-10
期刊：
Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control
影响因子：
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作者：
Xiaowu Sun;Haitham Khedr;Yasser Shoukry
通讯作者：
Xiaowu Sun;Haitham Khedr;Yasser Shoukry
影响因子：
2
作者：
Hoang-Dung Tran;Cai, Feiyang;Koutsoukos, Xenofon
通讯作者：
Koutsoukos, Xenofon