摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-一项关于机器人的新研究现在开始了。根据Ne wsRx记者在中国上海的新闻报道，研究表明：“视觉抓取技术在各种机器人应用中发挥着至关重要的作用，如工业自动化、仓储和物流。然而，目前的视觉抓取方法在应用于工业场景时面临着局限性。”新闻记者引用了上海海洋大学的一项研究:“只关注抓取目标所在的工作空间限制了摄像机提供额外环境信息的能力;另一方面,监控整个工作区域引入了无关数据,阻碍了准确的抓取姿态估计。”本文提出了一种全局摄像机和深度摄像机相结合的目标抓取方法,具体地说,我们引入了一种基于快速R-CN-GRCNN的双层检测机制,通过注意机制增强快速R-CNN,将全局摄像机聚焦在工件放置区域并检测出该区域内的目标物体,当机器人收到抓取工件的指令时,改进后的快速R-CNN识别工件并引导机器人朝向目标位置，机器人上的深度摄像机利用生成残差卷积神经网络确定抓取姿态并执行抓取动作。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Robotics is now availab le. According to news reporting from Shanghai, People's Republic of China, by Ne wsRx journalists, research stated, "Visual grasping technology plays a crucial r ole in various robotic applications, such as industrial automation, warehousing, and logistics. However, current visual grasping methods face limitations when a pplied in industrial scenarios." The news correspondents obtained a quote from the research from Shanghai Maritim e University, "Focusing solely on the workspace where the grasping target is loc ated restricts the camera's ability to provide additional environmental informat ion. On the other hand, monitoring the entire working area introduces irrelevant data and hinders accurate grasping pose estimation. In this paper, we propose a novel approach that combines a global camera and a depth camera to enable effic ient target grasping. Specifically, we introduce a dual-layer detection mechanis m based on Faster R-CNN-GRCNN. By enhancing the Faster R-CNN with attention mech anisms, we focus the global camera on the workpiece placement area and detect th e target object within that region. When the robot receives the command to grasp the workpiece, the improved Faster R-CNN recognizes the workpiece and guides th e robot towards the target location. Subsequently, the depth camera on the robot determines the grasping pose using Generative Residual Convolutional Neural Net work and performs the grasping action."
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