用深度学习让3轴并联机械臂自动打包餐食，成功率超80%
Experimental Study on Automatically Assembling Custom Catering Packages With a 3-DOF Delta Robot Using Deep Learning Methods
- 结合YOLOv5与FastSAM实现物体检测与分割
- 通过特征向量计算出旋转角度和抓取点，成功率达80%以上
- 首个针对波斯制造产品的定制化包装数据集，适合工业自动化研究者
本文首次开展基于三自由度Delta并联机器人搭载双指夹爪的餐饮包装自动化实验研究。创新性地引入深度学习方法，构建了包含1500张图像的定制数据集，聚焦波斯制造产品。采用YOLOv5进行目标检测，再利用FastSAM完成分割，生成分割掩码后计算物体旋转角度，并生成包围矩形。基于此矩形，提出一种基于特征向量的新几何方法计算两个抓取点。实验验证表明，该算法可实现实时检测、校准与全自动包装，整体自动抓取成功率超过80%，显著推动了机器人在包装自动化中的实际应用能力。
原文摘要 · Abstract (English)
This paper introduces a pioneering experimental study on the automated packing of a catering package using a two-fingered gripper affixed to a 3-degree-of-freedom Delta parallel robot. A distinctive contribution lies in the application of a deep learning approach to tackle this challenge. A custom dataset, comprising 1,500 images, is meticulously curated for this endeavor, representing a noteworthy initiative as the first dataset focusing on Persian-manufactured products. The study employs the YOLOV5 model for object detection, followed by segmentation using the FastSAM model. Subsequently, rotation angle calculation is facilitated with segmentation masks, and a rotated rectangle encapsulating the object is generated. This rectangle forms the basis for calculating two grasp points using a novel geometrical approach involving eigenvectors. An extensive experimental study validates the proposed model, where all pertinent information is seamlessly transmitted to the 3-DOF Delta parallel robot. The proposed algorithm ensures real-time detection, calibration, and the fully autonomous packing process of a catering package, boasting an impressive over 80\% success rate in automatic grasping. This study marks a significant stride in advancing the capabilities of robotic systems for practical applications in packaging automation.
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