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This paper introduces a forecasting scheme that utilizes critical slowing down (CSD) to predict controller-induced loss of control (LOC) in quadrotors, validated with real flight data from four different quadrotors. By deriving early warning signals of LOC without the need for system models, the method achieves a remarkable time-to-LOC forecast of up to 0.9 seconds, significantly outperforming existing recurrent neural network approaches in both accuracy and data reliance. The approach's generalizability across various quadrotor architectures and LOC scenarios highlights its potential for widespread application in aerial robotics.
Forecasting LOC in quadrotors can be achieved up to 0.9 seconds in advance using critical slowing down, without requiring system models or event data.
We develop a novel forecasting scheme to anticipate controller induced loss of control (LOC) in quadrotors and evaluate it on real LOC flight data from four different quadrotors. For this, early warning signals of LOC are derived using critical slowing down (CSD), a generic phenomenon shown to precede critical transitions across various complex ecological and biological systems. As such, our early warning indicators are generic in the sense that no system models are needed to facilitate forecasts of LOC. The approach is evaluated on real quadrotor flight data wherein LOC occurs due to unstable controller behavior arising from input-output delays. Our approach achieves a time-to-LOC forecast of up to 0.9 seconds before LOC occurs, outperforming state-of-the-art recurrent neural network quadrotor LOC forecasters in terms of detection accuracy and LOC data reliance. In particular, we leverage insights from CSD to accurately predict LOC without using data of the LOC event itself. Going further, we apply our forecasters without any re-parameterization to anticipate a different LOC scenario, quadrotor flyways, that occur on other quadrotors flying both indoors and outdoors. Despite these differences, our approach successfully detects LOC, demonstrating that it can generalize across controller architectures, quadrotors, and LOC scenarios.