基于深度学习的机械臂拾取物体模式识别解决方案

M. N. Anh, D. X. Bien
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引用次数: 0

摘要

本文提出了一种基于两个深度学习模块的模式识别解决方案,该方案支持机械手拾取和丢弃物体。第一个深度学习模块执行图像处理以识别已识别的物体。第二个模块用于基于第一个模块的识别结果训练物体拾取任务。为了验证所提出的模式识别方案的可行性,在一个真实的6自由度机械臂模型上进行了多次测试,关节变量的约束限制在-170°到170°之间。在对GeForce RTX 3080 GPU进行超过8小时的84次测试后,对象特征(形状和颜色),拾取或掉落位置发生变化,统计结果表明,机器人可以完全按照要求完成,使用低成本USB相机的准确率高达94%。
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A Solution of Pattern Recognition Based on Deep Learning for Robotic Manipulator to Pick Up and Drop Objects
This article presents the pattern recognition solution based on two deep learning modules supporting the robotic manipulator to pick up and drop objects. The first deep learning module performs image processing to recognize identified objects. The second module is used to train object pick and drop tasks based on the recognition results of the first module. To check the feasibility of the proposed pattern recognition solution, several tests are performed on a real robot arm model with 6 degrees of freedom with the constraints of joint variables limited from -170 degrees to 170 degrees. After performing 84 tests in more than 8 hours on GeForce RTX 3080 GPU with changes in object features (shape and colour), pick up or drop location, the statistical results show that the robot can be done exactly as required with up to 94% accuracy with a low-cost USB camera.
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