Self-attention SAC with vision-augmented LiDAR fusion for mapless robot navigation in dynamic environments
A mapless deep reinforcement learning (DRL) framework is proposed that fuses visual and 2D LiDAR data, integrates a self-attention soft actor-critic (SAC) architecture and a customized reward function, and achieves robust autonomous navigation and exploration without requiring obstacle priors.