一种多系统整合物料搬运机器人的设计与研究
王崑宇1，朱嘉晨2
1. 燕山大学里仁学院电气工程系，秦皇岛市，066000；
2. 燕山大学里仁学院机械工程系，秦皇岛市，066000
摘要：物料搬运机器人可以完成高危险、重复性的工作，在老龄化不断加深的社会背景下，我们研究和设计了一种基于多系统整合的智能物料搬运机器人。它整合了PLC、stm32等多个处理器，机器人能在指定场地内自主行走，通过扫描阅读货物清单上的二维码获取搬运任务，自主寻找、识别任务指定的物料，按要求将其搬运至指定的存放地点。机器人采用D-H机械臂建模，在MATLAB建模的基础上得出机械臂生产参数，使用Solidworks进行三维建模设计，通过3D打印对机械臂的部分零件进行初步制作。机器人采用模块化编程，利用PLC实现路径规划，结合OpenCV视觉系统模块和深度学习算法提高机器人搬运和抓取的准确性，机器人借助多系统模块控制，使其具有综合优势，提升工作效率，增强适应性，具有较高的生产应用价值。
关键词：智能物料搬运；PD控制机械臂；PLC路径规划；OpenCV视觉识别；D-H机械臂建模；深度学习算法；多系统模块化控制；二维码读取
Design and research of a multi-system integratedmaterial handling robot
Wang Kunyu1，Zhu Jiachen2
1. Yanshan University, Department of Electrical Engineering Liren College, Qinhuangdao City, 066000;
2. Yanshan University,Department of Mechanical Engineering Liren College, Qinhuangdao City, 066000
Abstract：Material handling robot can accomplish high-risk and repetitive work. Under the background of the aging society, an intelligent material handling robot based on multi-system integration is studied and designed. It integrates many processors such as PLC and stm32. The robot can walk independently in a designated site, obtain the handling task by scanning the two-dimensional code on the inventory, independently find and identify the material specified by the task, and carry it to the designated storage place according to the requirements. The robot uses D-H manipulator to model, obtains the production parameters of the manipulator on the basis of MATLAB modeling, uses Solidworks to carry out three-dimensional modeling design, and preliminarily fabricates. Some part of the manipulator through three-dimensional printing. The robot Uses modular programming and PLC to realize its path planning. Combined with OpenCV vision system module and deep learning algorithm, the accuracy of robot handling and grasping is improved. With the help of multi-system modular control, it has comprehensive advantages, can improve work efficiency and enhance adaptability, also has high production application value.
Keywords：Intelligent material handling; PD controls the robotic arm; PLC path planning;OpenCV visual recognition; D-H robotic arm modeling; Deep learning algorithm; Multi-system modular control;I-nigma reader