CBAM-ST-GCN: An enhanced DRL-based end-to-end visual navigation framework for mobile robot.

Abstract

Visual-based navigation for mobile robot poses significant challenges due to limited visual perception and the presence of unforeseen dynamic obstacles. Deep reinforcement learning (DRL) provides an end-to-end solution by directly mapping raw sensor data to control commands, offering high adaptability and reduced reliance on handcrafted rules. However, high-dimensional visual inputs and the non-stationarity introduced by dynamic obstacles easily make the policy learning of DRL difficult to convergent and unstable. In this paper, an enhanced end-to-end visual navigation framework is proposed for mobile robot operating in dynamic environments, denoted as CBAM-ST-GCN. A convolutional block attention module (CBAM) is introduced into the framework to enhance visual perception by assigning attention weights across spatial and temporal dimensions. Furthermore, a spatio-temporal graph convolutional network (ST-GCN) is designed to capture the behavior features of moving obstacles. In addition, a velocity obstacle (VO) method-based penalty term is incorporated into the reward function for the enhancement of collision avoidance. Extensive simulation results demonstrate that the proposed method achieves superior success rates and significantly higher convergence speed. Real-world experiments further validate the effectiveness and adaptability of the proposed approach in practical scenarios.