An Enhanced Composite Predictive Framework for UUV Autonomous Docking via Motion Pattern Decoupling

This paper proposes an advanced composite predictive framework leveraging motion pattern decoupling, designed to enhance the prediction accuracy and computational efficiency of three-dimensional relative distance in autonomous underwater docking operations for Unmanned Underwater Vehicles (UUVs). Initially, the framework employs a velocity discontinuity detection algorithm to partition the original three-dimensional trajectory data into kinematic-consistent subtrajectories characterized by uniform motion patterns. Subsequently, integrating Dynamic Time Warping (DTW) with the elbow method, the K-means algorithm is refined to facilitate trajectory pattern clustering and establish distinct pattern category labels. For each motion pattern identified, a customized Gated Recurrent Unit (GRU) predictive model is meticulously trained. During the online prediction phase, real-time kinematic states are extracted through a sliding window mechanism, and the K-Nearest Neighbors (KNN) classifier is adeptly utilized to select the most suitable GRU model for dynamic forecasting. Empirical analyses, grounded in 200 sets of authentic underwater docking experiment data, reveal that this framework markedly surpasses standalone LSTM and GRU models, as well as acoustic-optic sensors, in predictive precision. Furthermore, the single-step prediction latency consistently remains under 17 milliseconds, thus fulfilling the real-time requisites of high-dynamic conditions. This research furnishes theoretical and technical underpinnings for precise UUV docking in intricate underwater milieus.