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Safety and resilience are critical for autonomous unmanned aerial vehicles(UAVs). We introduce MAVFI, the micro aerial vehicles (MAVs) resilienceanalysis methodology to assess the effect of silent data corruption (SDC) onUAVs' mission metrics, such as flight time and success rate, for accuratelymeasuring system resilience. To enhance the safety and resilience of robotsystems bound by size, weight, and power (SWaP), we offer two low-overheadanomaly-based SDC detection and recovery algorithms based on Gaussianstatistical models and autoencoder neural networks. Our anomaly errorprotection techniques are validated in numerous simulated environments. Wedemonstrate that the autoencoder-based technique can recover up to all failurecases in our studied scenarios with a computational overhead of no more than0.0062%. Our application-aware resilience analysis framework, MAVFI, can beutilized to comprehensively test the resilience of other Robot Operating System(ROS)-based applications and is publicly available athttps://github.com/harvard-edge/MAVBench/tree/mavfi.

MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles