Kinematic self-grasping learning method and system based on simulated industrial robot

Abstract

The invention discloses a kinematic self-grasping learning method and system based on a simulated industrial robot, and belongs to the field of computer-aided manufacturing. In the method, robot grasping training is conducted based on a simulation environment with a reinforcement learning theory, and the simulation robot automatically acquires the position information of an object through an imagetaken by a camera to determine the grasping position of a robot end grasping tool; and meanwhile, the posture of the grasping tool is determined according to the shape and placement state of the to-be-grasped object in the observed image with an image processing method based on reinforcement learning, and finally objects which have different shapes and are randomly placed are successfully grasped. The grasping technology can be applied to many industry and life scenes and can reduce the complexity of grasping work programming of a traditional robot and improve the expansibility of a robot program, thereby greatly enlarging the application range of the robot and improving the working efficiency in actual production.

Description

technical field [0001] The invention belongs to the field of computer-aided manufacturing, and more specifically relates to a kinematics self-grabbing learning method and system based on a simulated industrial robot. Background technique [0002] Industrial robots represented by six-joint manipulators have become more perfect in function and application. Robots are now widely used in various tasks, such as spraying, palletizing, handling, packaging, welding, assembly and other tasks, most of which use robots to replace humans. work job. The use of robots has greatly liberated manpower, improved safety factors, and improved production efficiency and quality. [0003] However, the intelligence level of robots used in current industrial production is still relatively low. Even in a production line with a relatively high level of automation, the actions of the robot usually require the operator to teach the action in advance or need to perform offline programming for specific ...

AI Technical Summary

Problems solved by technology

This process not only requires a very high technical level of the operator, but also lacks scalability. Even if there is only a slight change in the shape, position, placement angle or background environment of the operation object, the system needs to be shut down, and the teaching should be performed again or offline. Programming, making complex modifications, wastes manpower while greatly reducing production efficiency and increasing production costs

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