融合Transformer与SAC算法的无人车视觉导航模型OA

To address the high perception and safety requirements of autonomous navigation tasks in real-world environ-ments,this paper proposes an unmanned ground vehicle(UGV)visual navigation model that integrates Transformer and SAC(Soft Actor-Critic)algorithms.This model utilizes a Vision Transformer(ViT)network to extract image features,which serve as state inputs for the reinforcement learning algorithm,thereby enhancing the UGV's perception capabilities.To mitigate safety issues arising from visual sensor failures,a pre-trained radar policy is employed as a low computational cost emergency fallback during the decision-making process.Simulation experiments conducted in Gazebo demonstrate that the visual navigation policy trained with Vision Transformer not only converges faster and achieves higher success rates but also ensures navigation stability through effective coordination of multi-modal strategies during sensor failures.Further-more,the trained model exhibits excellent adaptability and robustness across different environments.