An AUV route trajectory prediction method based on COMGRU
To address the issue of hysteresis in predicting the trajectory of autonomous underwater vehicles(AUVs)using neural networks,this paper proposes an improved gated recurrent unit(GRU)network based on information compression for multi-step voyage path prediction.The algorithm compresses geographical location information,which includes obstacle location information,sea current information,and space-time trajectory information near the AUV's voyage path.This compressed information is used as the input for the prediction network,enhancing the network's training efficiency.Experiments confirm that the algorithm effectively reduces hysteresis and achieves high accuracy in multi-step AUV trajectory prediction.
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