Visual 3D taking and placing method and system based on cooperative robot

Abstract

The invention provides a visual 3D taking and placing method and system based on a cooperative robot. The method comprises the steps that internal and external parameters of a camera of a binocular structured light three-dimensional scanner are calibrated; the hands and eyes of the cooperative robot are calibrated, and a calibration result matrix is obtained; a three-dimensional digital model of to-be-taken-and-placed target objects is collected; the calibrated binocular structured light three-dimensional scanner is adopted to obtain point cloud data of the to-be-taken-and-placed target objects which are stacked in a scattered mode, and the point cloud is segmented to obtain scene point clouds of the multiple to-be-taken-and-placed target objects; the to-be-taken-and-placed target object with the highest grabbing success rate is selected as a grabbing target according to the scene point clouds of the multiple to-be-taken-and-placed target objects; the three-dimensional digital model ofthe grabbing target and scene point pair features are registered, pre-defined taking and placing pose points are registered into a scene, and a registered pose estimation result is obtained and serves as a grabbing pose of the grabbing target; and a preliminary grabbing path track of the cooperative robot is planned. The target object can be accurately recognized, and the grabbing positioning precision is high.

Description

technical field [0001] The invention relates to the technical field of visual 3D pick-and-place, in particular to a visual 3D pick-and-place method and system based on a collaborative robot. Background technique [0002] With the automation and intelligence of industrial manufacturing and logistics transportation, the industrial robot pick-and-place system with multi-sensor integration is the core force in the future automated intelligent manufacturing and intelligent logistics fields. At present, the industrial robot pick-and-place system is mainly used in the fields of workpiece assembly, material loading, product handling, object sorting, defect detection, and packaging. In the traditional structured environment, the robot pick-and-place system that performs single repetitive operations through offline programming cannot complete the scattered stacking of target objects, and at the same time, it cannot estimate the pose of the captured target in the scene. It is just a gr...

AI Technical Summary

Problems solved by technology

[0004] Most of the existing visual perception processes can only be used for 2D pose estimation of flat single-target objects, which are easily affected by factors such as lighting and background environment, and are difficult to cope with actual situations such as scene lighting changes, occlusion stacking, etc.

At present, the visual perception system based on deep learning method that has made great progress has a long production cycle and high difficulty in making network training data sets; at the same time, the network model has weak generalization ability and low robustness, which is not conducive to practical application. indoor scene

[0005] However, for existing robot systems, the upper limit of installation and use is high, the programming process is cumbersome, human-machine cooperation is not emphasized, the trajectory is mostly taught manually, without trajectory planning, the operation efficiency is low, and the reliability is poor. Job failed, can only be used in structured environments

[0006] There is a lack of an automated and intelligent pick-and-place system based on collaborative robots and high-precision vision algorithms in the prior art

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