Autonomous mobile robots share social spaces with humans, usually working together for domestic or professional tasks. Cyber security breaches in such robots undermine the trust between humans and robots. In this paper, we investigate how to apprehend and inflict security threats at the design and implementation stage of an autonomous mobile robot. To this end, we leverage the idea of directed fuzzing and design RoboFuzzthat systematically tests an autonomous mobile robot in line with the robot’s states and the surrounding environment. The methodology of RoboFuzzis to study critical environmental parameters affecting the robot’s state transitions and subject the robot control program with rational but harmful sensor values so as to compromise the robot. Furthermore, we develop detection and mitigation algorithms to counteract the impact of RoboFuzz. The difficulties mainly lie in the trade-off among limited computation resources, timely detection and the retention of work efficiency in mitigation. In particular, we propose detection and mitigation methods that take advantage of historical records of obstacles to detect inconsistent obstacle appearances regarding untrustworthy sensor values and navigate the movable robot to continue moving so as to carry on a planned task. By doing so, we manage to maintain a low cost for detection and mitigation but also retain the robot’s work efficacy. We have prototyped the bundle of RoboFuzz, detection and mitigation algorithms in a real-world movable robot. Experimental results confirm that RoboFuzzmakes a success rate of up to 93.3% in imposing concrete threats to the robot while the overall loss of work efficacy is merely 4.1% at the mitigation mode.

自主移动机器人与人类共享社交空间，通常一起工作以完成家庭或专业任务。此类机器人的网络安全漏洞破坏了人与机器人之间的信任。在本文中，我们研究了如何在自主移动机器人的设计和实施阶段理解和施加安全威胁。为此，我们利用定向模糊的想法和设计机器人˚F uzz系统地测试与机器人的状态线的自主移动机器人和周围环境。的方法机器人˚F uzz将研究影响机器人状态转换的关键环境参数，并使机器人控制程序受到合理但有害的传感器值的影响，从而危害到机器人。此外，我们开发检测和缓解算法来抵消的影响机器人˚F uzz。困难主要在于有限的计算资源之间的权衡，及时检测以及在缓解中保持工作效率。尤其是，我们提出了一种检测和缓解方法，该方法利用障碍物的历史记录来检测与不可靠传感器值有关的不一致的障碍物出现，并导航可移动机器人以继续移动以执行计划的任务。这样，我们设法保持较低的检测和缓解成本，同时又保持了机器人的工作效率。我们都在原型束机器人˚F uzz，检测和缓解算法在现实世界中移动机器人。实验结果证实，机器人˚F uzz在对机器人施加具体威胁时，成功率高达93.3％，而在缓解模式下，总体工作效率损失仅为4.1％。