Safety and resilience are critical for autonomous unmanned aerial vehicles (UAVs). We introduce MAVFI, the micro aerial vehicles (MAVs) resilience analysis methodology to assess the effect of silent data corruption (SDC) on UAVs' mission metrics, such as flight time and success rate, for accurately measuring system resilience. To enhance the safety and resilience of robot systems bound by size, weight, and power (SWaP), we offer two low-overhead anomaly-based SDC detection and recovery algorithms based on Gaussian statistical models and autoencoder neural networks. Our anomaly error protection techniques are validated in numerous simulated environments. We demonstrate that the autoencoder-based technique can recover up to all failure cases in our studied scenarios with a computational overhead of no more than 0.0062%. Our application-aware resilience analysis framework, MAVFI, can be utilized to comprehensively test the resilience of other Robot Operating System (ROS)-based applications and is publicly available at https://github.com/harvard-edge/MAVBench/tree/mavfi.
翻译:安全性和复原力是自主无人驾驶飞行器(无人驾驶飞行器)的关键。我们引入了微型飞行器(MAVFI)抗御能力分析方法,即微型飞行器(MAVFI),以评估无声数据腐败对无人驾驶飞行器飞行任务测量标准(如飞行时间和成功率)的影响,以便准确测量系统的抗御能力。为了提高受大小、重量和功率约束的机器人系统的安全和抗御能力(SWAP),我们提供基于高斯统计模型和自动神经网络的基于异常的SDC检测和恢复算法的两种低端超标检测和算法。我们的异常错误保护技术在许多模拟环境中得到验证。我们证明,基于自动计算器的技术可以恢复到我们所研究的假设情景中的所有故障案例,计算间接率不超过0.0062%。我们的应用抗御能力分析框架(MAVFI)可用于全面测试其他机器人操作系统(ROS)应用的抗御力,并公布在https://github.com/harvad-se/MAVBench/tree/mavfi。