Robot intelligent grabbing method based on digital twinning and deep neural network

Abstract

The invention discloses a robot intelligent grabbing method based on digital twinning and a deep neural network. The robot intelligent grabbing method comprises a physical grabbing environment, a virtual recognition environment and a core neural network part. The physical environment is composed of a depth camera, a robot, a mechanical claw and a grabbed object and is a main executing mechanism for grabbing. The virtual recognition environment is composed of a point cloud file constructed by the depth camera and related postures of a robot and a claw, and is a virtual environment set of a robot state, a mechanical claw position, a camera posture and an object placement position. The core neural network comprises a grabbing generation network and a grabbing recognition network, and the grabbing mode is sampled and judged to generate an optimal grabbing posture. According to the robot intelligent grabbing method, the optimal grabbing position and posture can be rapidly and efficiently judged based on the color-depth image collected by the camera.

Description

technical field [0001] The invention belongs to the field of digital twin intelligent manufacturing, and in particular relates to a robot intelligent grasping method based on digital twin and deep neural network. Background technique [0002] Digital Twin digital twin technology: it makes full use of data such as physical models, sensor updates, and operation history, integrates multi-disciplinary, multi-physical quantities, multi-scale, and multi-probability simulation processes, and completes the mapping of the physical world in the virtual world, thereby reflecting the corresponding The whole life cycle process of physical equipment. The digital twin can be regarded as a digital mapping system of one or more important and interdependent equipment systems, which is the link of interaction and integration between the physical world and the virtual world. [0003] With the development of Industry 3.0, the initial automated robots undertake repetitive, boring and low-intelli...

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Problems solved by technology

[0004] However, with the trend and development of Industry 4.0, the above methods have revealed the following problems: robots are not only required to be able to perform repetitive tasks, but are also expected to be able to complete complex tasks to a certain extent and have the ability to respond to environmental changes

When the posture of the object is messy, the grasping work will become quite difficult, that is, the traditional open-loop control of the robotic arm has basically zero resistance to environmental changes, and it is difficult to cope with the changing environment and Smart Production Requirements

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