Cascaded convolutional neural network-based quick detection method of irregular-object grasping pose of robot

A convolutional neural network and detection method technology, which is applied in the field of robot vision technology detection and grasping control, can solve problems such as not being well used to solve robot grasping posture detection, and overcome the influence of grasping posture detection. , improve real-time performance, improve the effect of grasping detection accuracy

Inactive Publication Date: 2018-09-07
SOUTHEAST UNIV
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Problems solved by technology

[0008] In short, deep learning technology has been initially applied in the field of robotics, but it has not been well used to solve the problem of robot grasping attitude detection, especially how to

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  • Cascaded convolutional neural network-based quick detection method of irregular-object grasping pose of robot
  • Cascaded convolutional neural network-based quick detection method of irregular-object grasping pose of robot
  • Cascaded convolutional neural network-based quick detection method of irregular-object grasping pose of robot

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Embodiment

[0041] The robot and camera configuration adopted in the embodiment are as follows: figure 1 shown, including a low-cost top-down color camera (resolution ), a UR5 robot that has been calibrated by hand-eye. The computer configuration used for model training and experiment implementation is Intel(R) Core(TM) i7 3.40GHz CPU, NVIDIA GeForce GTX 1080TI graphics card, 16GB memory, and the operating system is Ubuntu 16.04.

[0042] figure 2 The definition relations of each coordinate system are given in . In order to facilitate the correspondence between the grasping detection results and the robot's grasping pose, the grasping pose detection results under the image are represented by a simplified "dot-line method". The center point of the grabbing position in the image is recorded in the image coordinate system as , corresponding to the midpoint of the line connecting the two fingers of the robot end effector; the grasping center line in the image corresponds to the line co...

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Abstract

The invention relates to a cascaded convolutional neural network-based quick detection method of an irregular-object grasping pose of a robot. Firstly, a cascaded-type two-stage convolutional-neural-network model of a position-attitude rough-to-fine form is constructed, in a first stage, a region-based fully convolutional network (R-FCN) is adopted to realize grasping positioning and rough estimation of a grasping angle, and in a second stage, accurate calculation of the grasping angle is realized through constructing a new Angle-Net model; and then current scene images containing to-be-grasped objects are collected to be used as original on-site image samples to be used for training, the two-stage convolutional-neural-network model is trained by means of a transfer learning mechanism, then each collected monocular color image is input to the cascaded-type two-stage convolutional-neural-network model in online running, and finally, an end executor of the robot is driven by an obtainedgrasping position and attitude for object grasping control. According to the method, grasping detection accuracy is high, detection speed of the irregular-object grasping pose of the robot is effectively increased, and real-time performance of running of a grasping attitude detection algorithm is improved.

Description

technical field [0001] The invention relates to a method for robot autonomous grasping posture detection, specifically a method for rapidly detecting the posture and posture of a robot's irregular object grasping based on a cascaded convolutional neural network, which belongs to robot vision technology detection and grasping control technology field. Background technique [0002] In robot sorting, handling and other grasping tasks, Planar Grasp, including Top-grasp and Side-grasp, is the most commonly used grasping strategy for robots. For unknown irregular objects with arbitrary poses, in scenes with uneven illumination and complex backgrounds, how to use low-cost monocular cameras to achieve fast and reliable robot autonomous grasp pose detection is a great challenge. [0003] Robot autonomous grasping attitude planning methods can be divided into two categories according to different perception information: one is the grasping attitude estimation based on the object mode...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06T7/70
CPCG06T7/70G06T2207/10024G06T2207/20084G06T2207/20081G06T2207/30164G06N3/045
Inventor 钱堃夏晶刘环张晓博马家乐康栓紧
Owner SOUTHEAST UNIV
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