Pushing and grabbing collaborative sorting network based on double viewing angles and sorting method and system thereof

Abstract

The invention discloses a pushing and grabbing collaborative sorting network based on double viewing angles and a sorting method and system thereof. The trained pushing and grabbing collaborative sorting network comprises a pushing full convolutional network and a grabbing full convolutional network, and the network is applied to robot pushing and grabbing collaborative sorting. The sorting methodcomprises the following steps of correspondingly acquiring point cloud graphs of an object scene to be sorted from two viewing angles, rotating a top view of the point cloud graphs, correspondingly inputting a plurality of rotating images into the pushing full convolutional network and the grabbing full convolutional network to obtain two thermodynamic graphs with Q values output by the networks,and selecting the thermodynamic diagram with the larger Q value as a final thermodynamic diagram; and according to the pixel point corresponding to the maximum Q value in the thermodynamic diagram and the rotation angle of the rotation image corresponding to the thermodynamic diagram, controlling the robot to execute the sorting action of the network corresponding to the thermodynamic diagram, and then completing sorting. According to the sorting method, double viewing angles are combined with deep Q learning, so that the grabbing success rate is high and the generalization ability is high inthe face of a disordered stacking scene.

Description

technical field [0001] The invention belongs to the technical field of robot application, and more specifically relates to a push-and-grab collaborative sorting network based on dual perspectives and a sorting method and system thereof. Background technique [0002] Object grasping is the main means of robot sorting operation. The level of robot grasping operation ability directly determines the efficiency of sorting. Robust and efficient sorting of objects is a current research hotspot in robotics. Most of the current object capture methods are aimed at non-stacked object scenes, using cameras to capture scene pictures, and combining traditional image processing methods and machine learning methods to segment and recognize objects. However, as industrial application scenarios become more and more complex, there are often situations that are not conducive to sorting, such as multiple target objects, arbitrary placement of object poses, mutual contact and occlusion between mu...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the above defects or improvement needs of the prior art, the present invention provides a dual-view based push-and-grab collaborative sorting network and its sorting method and system, thereby solving the problem of grasping when faced with messy stacking scenes in the prior art. Technical issues with low success rate and poor generalization ability

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