Energy Optimization for Autonomous Mobile Robot Path Planning Based on Deep Reinforcement Learning

At present, energy consumption is one of the main bottlenecks in autonomous mobile robot development. To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments, this paper proposes an Attention-Enhanced Dueling Deep Q-Network (AD-Dueling DQN), which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework. A multi-objective reward function, centered on energy efficiency, is designed to comprehensively consider path length, terrain slope, motion smoothness, and obstacle avoidance, enabling optimal low-energy trajectory generation in 3D space from the source. The incorporation of a multi-head attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths. Additionally, the prioritized experience replay mechanism accelerates learning from key decision-making experiences, suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly. The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios. Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments. Moreover, the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms, highlighting its global optimization capability under energy-aware objectives in complex terrains. This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.