The security issue of mobile robots has attracted considerable attention in recent years. In this paper, we propose an intelligent physical attack to trap mobile robots into a preset position by learning the obstacle-avoidance mechanism from external observation. The salient novelty of our work lies in revealing the possibility that physical-based attacks with intelligent and advanced design can present real threats, while without prior knowledge of the system dynamics or access to the internal system. This kind of attack cannot be handled by countermeasures in traditional cyberspace security. To practice, the cornerstone of the proposed attack is to actively explore the complex interaction characteristic of the victim robot with the environment, and learn the obstacle-avoidance knowledge exhibited in the limited observations of its behaviors. Then, we propose shortest-path and hands-off attack algorithms to find efficient attack paths from the tremendous motion space, achieving the driving-to-trap goal with low costs in terms of path length and activity period, respectively. The convergence of the algorithms is proved and the attack performance bounds are further derived. Extensive simulations and real-life experiments illustrate the effectiveness of the proposed attack, beckoning future investigation for the new physical threats and defense on robotic systems.
翻译:近年来,移动机器人的安全问题引起了相当多的关注。在本文中,我们建议进行明智的物理攻击,通过从外部观察中学习避免障碍的机制,将移动机器人困入一个预设位置。我们工作的突出新颖之处在于揭示以智能和先进设计进行的物理攻击有可能带来真正的威胁,而没有事先对系统动态或进入内部系统的知识,这种攻击无法由传统网络空间安全的对策来应对。在实践中,拟议攻击的基石是积极探索受害者机器人与环境的复杂互动特征,并学习在其有限的行为观察中显示的避免障碍的知识。然后,我们提出最短的路径和亲手攻击算法,以便从巨大的运动空间找到有效的攻击路径,分别达到在路径长度和活动期间成本较低的驾驶到控制目标。算法的趋同和攻击性性能约束被进一步推导出。广泛的模拟和现实生活实验说明了拟议攻击的效果,对新的物理威胁和机器人系统的防御进行未来调查。