Design of Intelligent Control System for Autonomous Transport Vehicle Based on Deep Learning

In response to the problems of low path planning efficiency, insufficient navigation accuracy, and limited obstacle avoidance ability in complex environments caused by insufficient perception ability and algorithm optimization of existing autonomous transport vehicles, this article introduces deep learning technology and designs an intelligent control system based on Convolutional Neural Network (CNN). Firstly, the system utilizes CNN to obtain real-time surrounding environment information through camera and sensor fusion technology, and performs target detection and obstacle avoidance strategy selection; then, the LSTM (Long Short Term Memory) network combines historical path data such as location, driving trajectory, speed changes, obstacle position, execution time, and real-time perception results to optimize the planned path; finally, the system is trained using a deep Q-network to enhance the decision-making ability of the car in dynamic environments. The experimental results show that the designed intelligent control system based on deep learning significantly improves the environmental perception ability and path planning efficiency of autonomous transport vehicles in response to the problems of insufficient perception ability and algorithm optimization. Specifically, the car is able to more accurately detect and avoid obstacles in complex environments, with a 30% increase in navigation accuracy and a 20% reduction in path planning time. Compared with traditional algorithms, this system performs more flexibly in dealing with dynamic environmental changes, effectively solving the problems of insufficient navigation accuracy and limited obstacle avoidance ability. The intelligent control system for autonomous transport vehicles based on deep learning not only improves the efficiency and accuracy of path planning, but also effectively enhances its obstacle avoidance ability, making it suitable for complex and changing transportation environments.