AGV forklift tray positioning and forking method based on 3D depth vision

A deep vision, pallet positioning technology, applied in navigation, instrumentation, electric/hybrid, etc., can solve problems such as the inability to meet the strict requirements of AGV forklift safety and operation accuracy

Active Publication Date: 2021-08-17
SHANGHAI UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

Therefore, the above-mentioned series of technical defects cannot meet the strict requirements of the production environment for the safety and operation accuracy of AGV forklifts.

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  • AGV forklift tray positioning and forking method based on 3D depth vision
  • AGV forklift tray positioning and forking method based on 3D depth vision
  • AGV forklift tray positioning and forking method based on 3D depth vision

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Embodiment Construction

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0037] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0038] The present invention provides an AGV forklift pallet positioning and fork picking method based on 3D depth vision, as shown in the attached figure 1 shown, including:

[0039] S1. The present invention ad...

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Abstract

The invention discloses an AGV forklift tray positioning and forking method based on 3D depth vision, and the method comprises the steps: firstly combining a normal vector plane and signal intensity gray segmentation, carrying out the iterative comparison calculation through employing a tray morphological template, and extracting a tray hole site to recognize the type of a tray; then calculating a prong outer edge linear equation through a convolution mask detector according to a tray form constraint condition; and finally, calculating vertical and horizontal space Euclidean distances from the boundary point of the tray to the extension line of the prong so as to obtain an absolute pose of the prong relative to the tray and judge whether the prong can work safely or not. According to the method, tray hole site recognition and real-time pose accurate measurement of the opposite prongs in a complex operation scene are effectively achieved, the system is not affected by physical factors such as color, size and illumination, and high applicability is achieved.

Description

technical field [0001] The invention relates to the technical field of intelligent warehousing logistics and robot vision, in particular to an AGV forklift pallet positioning and forking method based on 3D depth vision. Background technique [0002] In recent years, with the rise of intelligent logistics and unmanned warehouses, the automatic handling and sorting of goods by AGV unmanned forklifts according to the program has become a research and application hotspot. At present, the unmanned transformation of domestic industrial forklifts mainly adopts laser radar, infrared sensing and other solutions. Due to factors such as vibration, random positioning errors, and insufficient coverage of laser sensors during the process of forklift picking up pallets, it is impossible to ensure that the forklift tines are aligned with the pallet. There are potential safety hazards. Therefore, it is urgent to develop a high-accuracy intelligent detection and positioning method for logist...

Claims

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Application Information

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IPC IPC(8): G01C21/00G01B11/00
CPCG01C21/00G01B11/002Y02P90/60
Inventor 王海宽石迁周文举盛志远杜大军
Owner SHANGHAI UNIV
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