Research on Robot Visual Obstacle Avoidance System Based on Neural Networks

With the continuous progress of science and technology, robotics has been widely used in several key fields, among which visual obstacle avoidance system is crucial to enhance the autonomous navigation and environmental adaptation ability of robots. This study focuses on a neural network-based visual obstacle avoidance system for robots, aiming to optimize the network structure, improve the algorithm and evaluate the performance in practical applications to enhance the robot's obstacle avoidance ability and safety in complex environments. The study firstly reviews the diversified framework of robot obstacle avoidance technology and analyzes the advantages and disadvantages of traditional sensors and visual obstacle avoidance technology. Subsequently, the basic principles of neural network and its application in robot visual obstacle avoidance system, including the principle of neural network inverse system, are discussed in depth. On this basis, an efficient and accurate visual obstacle avoidance system for undersea vehicles is designed, which adopts a layered architecture covering four major layers, namely, hardware interface, driver control, data processing and application interaction, and elaborates on the layout and calibration of the visual sensors, the design of the neural network model and the obstacle avoidance path planning algorithm. Experimental validation shows that the system can accurately recognize obstacles and plan safe and efficient paths, and the neural network shows excellent performance in obstacle detection, recognition and path planning. This study not only analyzes the principle of neural network inverse system, but also proposes the obstacle avoidance path planning algorithm based on it, which contributes new ideas and methods for the progress of robot visual obstacle avoidance technology.