Intelligent type barrier avoiding grabbing system based on deep learning

Abstract

The invention discloses an intelligent type barrier avoiding grabbing system based on deep learning. The intelligent type barrier avoiding grabbing system based on deep learning comprises a wireless base station unit, a binocular camera, a three-axis pan-tilt unit, an Mecanum system unit, a manipulator unit, a power source and an industrial computer; the power source is connected with the binocular camera, the three-axis pan-tilt unit, the Mecanum system unit, the manipulator unit and the industrial computer; the industrial computer is connected with an external terminal through the wireless base station unit; the binocular camera, the three-axis pan-tilt unit, the Mecanum system unit and the manipulator unit are all connected with the industrial computer; and the three-axis pan-tilt unitis connected with the binocular camera. The system can automatically recognize and lock a target object, barrier avoiding tracing is combined with a manipulator to grab a target object under a complicated background, the learning capacity is achieved, work can be independently completed, human intervention is not needed, it is achieved that the target object is automatically picked through the manipulator, the applying range of the intelligent type barrier avoiding grabbing system based on deep learning is widened, and the intelligent degree of the intelligent type barrier avoiding grabbing system based on deep learning is improved.

Description

technical field

[0001] The invention belongs to the field of artificial intelligence, in particular to an intelligent obstacle avoidance and grasping system based on deep learning. Background technique

[0002] Object autonomous recognition and obstacle avoidance grasping based on machine learning and machine vision have always been the research focus in the field of artificial intelligence. Binocular stereo vision technology can flexibly obtain the stereo information of the scene under various conditions. Compared with monocular vision, it has more advantages. With incomparable advantages, it is the frontier research direction in the field of image processing and machine vision. In robot grasping tasks, pure object recognition is not enough to provide sufficient grasping information, but must be closely combined with accurate object localization and machine learning. The current grasping technology is facing many difficulties, such as dynamic environment, lighting, complex...

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