Multi-Level Attention Dueling Double Deep Q-Network for Local Path Planning

Deep reinforcement learning (DRL) has shown considerable potential in local path planning for autonomous robots. However, existing DRL methods still suffer from limited training efficiency, poor generalization, and weak sim-to-real transferability in complex environments. To address these issues, this paper proposes a Multi-Level Attention Dueling Double Deep Q-Network (MLA-D3QN) framework, which progressively enhances feature extraction, spatial perception, and modality fusion through three attention levels: rule-based attention for obstacle contour extraction, implicit neural multi-scale spatial attention for environment perception, and bidirectional cross-attention for multi-modal feature alignment. Simulation results show that MLA-D3QN outperforms baseline and comparison methods in terms of convergence speed and average reward. Real-world experiments are conducted on a Scout mini platform with 50 trials in simple task scenarios (sparse obstacles, short distance) and 50 trials in complex task scenarios (dense obstacles, long distance). The proposed method achieves success rates of 98% in simple tasks and 94% in complex tasks. Compared to CNN-D3QN and D3QN, MLA-D3QN improves success rates by 10 percentage points (vs. CNN-D3QN) and 38 percentage points (vs. D3QN) in simple tasks, and by 34 percentage points (vs. CNN-D3QN) and 84 percentage points (vs. D3QN) in complex tasks. Path costs are reduced by 24.0% (vs. CNN-D3QN) and 59.9% (vs. D3QN). These results validate the effectiveness of MLA-D3QN in improving generalization and sim-to-real transferability for local path planning in complex environments.