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：安全性和韧性对于自主无人飞行器（UAV）至关重要。本文提出MAVFI，一种微型飞行器（MAV）韧性分析方法，用于评估静默数据损坏（SDC）对无人机任务指标（如飞行时间与成功率）的影响，从而精确衡量系统韧性。为提升受尺寸、重量和功率（SWaP）约束的机器人系统的安全性与韧性，我们提供两种基于高斯统计模型与自编码器神经网络的低开销异常检测与恢复算法。所提出的异常错误防护技术已在多个仿真环境中得到验证。实验表明，基于自编码器的技术能在计算开销不超过0.0062%的前提下，恢复所研究场景中高达全部故障案例。所提出的应用感知韧性分析框架MAVFI可用于全面测试其他基于机器人操作系统（ROS）应用的韧性，并已开源发布于https://github.com/harvard-edge/MAVBench/tree/mavfi。