Research on Intelligent Control System for Industrial Robots Based on Deep Learning

Le résumé fourni par la source

In the contemporary landscape where industrial automation is advancing at an accelerated pace, there is an imperative need for the intelligent upgrading of industrial robots. The paper focuses on the deep learning-based intelligent control system of industrial robots, analyzing the application of deep learning core algorithms in perception, motion control, and decision-making optimization. The system architecture covers the perception, control, and decision-making layers, using deep learning algorithms to integrate and process multimodal sensory information. The system also innovates the motion control strategy, combining deep learning and traditional control methods to optimize the robot's motion path and control accuracy. Furthermore, the intelligent decision-mak ing model is built to make reasonable decisions based on sensory information, improving the robot's ability to cope with complex tasks and environments. After experimental verification, the intelligent control system significantly improves the working efficiency, precision, and adaptability of industrial robots.

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