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Aiming at traditional manipulators that are limited to mechanized grasping of specific objects at fixed positions and postures according to a set process, an intelligent grasping system for non-specific objects based on machine vision is designed. The system locates the object on the image captured by the depth camera through the convolutional neural network, and predicts a reliable grasping position of the object on this image. The system transmits the information of grasping position to the manipulator, and the manipulator grasps the target object. The system is based on the robot operating system, and the necessary information is transmitted between the hardware through the topic of the robot operating system. The final experimental results show that through the improved rapid-exploration random tree algorithm, the manipulator can effectively capture non-specific objects in different positions and postures in real time, which improves the autonomy of the manipulator and makes up for the shortcomings of traditional manipulators.