Research on motion planning algorithm of industrial robot based on deep learning

In the field of industrial robots, motion planning is a critical component to ensure that robots effectively perform tasks. This paper investigates motion planning algorithms based on deep learning, focusing on how to integrate two existing algorithms into a new one to enhance the performance of motion planning for industrial robots. We chose reinforcement learning and deep neural networks (DNN) as the foundational computational methods. First, reinforcement learning is used to configure the robot's decision-making strategy, enabling it to adaptively adjust its movement paths in complex environments; meanwhile, deep neural networks are employed to process large-scale input data, improving the decision-making process through training and feature extraction. Leveraging the strengths of these two algorithms, we propose a novel motion planning algorithm that combines the adaptive learning capabilities of reinforcement learning with the powerful expressive power of deep neural networks. In the experimental section, we simulated various industrial scenarios and compared our new algorithm with traditional motion planning algorithms, demonstrating significant improvements in path planning accuracy, computational efficiency, and robustness. The results show that the proposed motion planning algorithm, which integrates reinforcement learning and deep neural networks, not only effectively handles dynamic work environments but also reduces the time required for path optimization, providing a more efficient solution for the application of industrial robots in complex tasks.