  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.
