首页|Studies Conducted at Shanghai Maritime University on Robotics Recently Reported (Vision-based Robotic Grasping Using Faster Rcnn- grcnn Dual-layer Detection Mec hanism)
Studies Conducted at Shanghai Maritime University on Robotics Recently Reported (Vision-based Robotic Grasping Using Faster Rcnn- grcnn Dual-layer Detection Mec hanism)
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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."
ShanghaiPeople's Republic of ChinaAs iaEmerging TechnologiesMachine LearningRobotRoboticsRobotsShanghai M aritime University
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