A CBAM-ResNet Based PPO Framework for Safe Navigation in Dynamic Pedestrian Environments

Safe and efficient navigation in dynamic pedestrian environments remains a significant challenge for mobile robots, particularly due to rapidly changing scenarios and complex multi-agent interactions. This paper proposes CBRNPPO, a deep reinforcement learning-based navigation framework designed to address these challenges. The framework incorporates a ResNet-based feature extractor enhanced with the Convolutional Block Attention Module (CBAM) to improve both perception and decision-making. By fusing multimodal inputs—including LiDAR scans, pedestrian velocity maps, and target direction vectors—the model effectively captures rich semantic representations of dynamic obstacles. The CBAM module adaptively emphasizes critical spatial and channelwise features, enhancing robustness in densely populated environments. Experimental results demonstrate that in scenarios with 35 pedestrians, CBRN-PPO achieves a 93% success rate in obstacle avoidance, outperforming the A1-RD baseline by 15%. Furthermore, it improves path efficiency by 38% and average navigation speed by 34%. These results highlight the effectiveness and robustness of CBRN-PPO as a navigation solution for autonomous mobile robots operating in complex dynamic environments.