Transformer-Based Dual-Channel Self-Attention for UUV Autonomous Collision Avoidance

Abstract

Autonomous collision avoidance is a critical technology in intelligent control, which is of great significance for autonomous navigation and operation of Unmanned Underwater Vehicles (UUVs). To enhance the autonomy of UUV collision avoidance and improve its adaptability to unstable forward-looking sonar observation and uncertain environments, we propose a novel Transformer-based Dual-Channel Self-attention (TDCS) architecture for UUV collision avoidance. TDCS is a network architecture composed of two encoders and a decoder, which integrates dynamic/static obstacle recognition, obstacle motion prediction, collision risk assessment, and collision avoidance decision. The two encoders extract the observation features of different sensors in parallel, and this dual-channel structure avoids the mutual interference of input information. Moreover, the two encoders are beneficial for adaptive learning to focus on sensor information with varying forms of representation. The fusion layer integrates the bimodal features extracted from different sensors by the dual-channel encoder. Finally, the fusion features and historical decisions are recombined through the decoder to form a new decision.