Deep Reinforcement Learning for Path Optimization of Industrial Robots

This paper proposes a path optimization method for industrial robots based on deep reinforcement learning, which integrates the advantages of deep neural networks and reinforcement learning to effectively address the limitations of traditional path planning approaches in complex dynamic environments. By constructing appropriate reward functions and state space representations, the method achieves path optimization under multiple constraints including obstacle avoidance, energy efficiency, and time optimization. We design a Deep Q-learning framework incorporating prioritized experience replay and a double network structure, while representing robot state information through multimodal fusion. Across three experimental scenarios of varying complexity, compared to traditional algorithms, the proposed method improves path smoothness by 23.456%, reduces energy consumption by 18.723%, and shortens task completion time by 15.891%. Ablation experiments further verify the contribution of each component to performance, particularly highlighting the significant impact of smoothness rewards and prioritized experience replay on final results. This research provides a novel solution for the efficient operation of industrial robots in complex environments and establishes a foundation for the application of deep reinforcement learning in robot path planning.