基于机器视觉的分类分拣机械臂系统的设计与开发

IF 1.2 Q3 ENGINEERING, MECHANICAL
V. Cong, L. Hanh, L. Phuong, D. Duy
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引用次数: 11

摘要

本文的主要重点是设计和开发一个基于机器视觉的基于形状和大小的物体分类和分拣的双机械臂系统。该系统采用一主二从的低成本、高性能的分级控制系统。每个从机是一个基于微控制器的机器人控制器,接收主机的指令,独立控制机械臂。master是一种嵌入式计算机,用于图像处理、运动学计算和通信。提出了一种简单高效的图像处理算法,可以实时实现,有助于缩短排序过程的时间。该方法采用轮廓查找、边界提取、质心算法、形状阈值等一系列算法进行目标识别和噪声消除。通过求解线性方程组来估计物体的三维坐标。机器人关节的运动被规划成具有加速/减速阶段的梯形轮廓,从而帮助机器人平稳移动并减少振动。实验验证了机器人视觉系统在分拣过程中的有效性和准确性。该系统可用于工业过程中减少生产线完成任务所需的时间,从而提高生产线的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and development of robot arm system for classification and sorting using machine vision
The main focus of this paper is to design and develop a system of two robot arms for classifying and sorting objects based on shape and size using machine vision. The system uses a low-cost and high-performance hierarchical control system including one master and two slaves. Each slave is a robot controller based on a microcontroller that receives commands from the master to control the robot arm independently. The master is an embedded computer used for image processing, kinematic calculations, and communication. A simple and efficient image processing algorithm is proposed that can be implemented in real-time, helping to shorten the time of the sorting process. The proposed method uses a series of algorithms including contour finding, border extraction, centroid algorithm, and shape threshold to recognize objects and eliminate noise. The 3D coordinates of objects are estimated just by solving a linear equation system. Movements of the robot's joints are planned to follow a trapezoidal profile with the acceleration/deceleration phase, thus helping the robots move smoothly and reduce vibration. Experimental evaluation reveals the effectiveness and accuracy of the robotic vision system in the sorting process. The system can be used in the industrial process to reduce the required time to achieve the task of the production line, leading to improve the performance of the production line.
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来源期刊
FME Transactions
FME Transactions ENGINEERING, MECHANICAL-
CiteScore
3.60
自引率
31.20%
发文量
24
审稿时长
12 weeks
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