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- W4382246145 abstract "The robot can work stably and reliably on the assembly line with fixed position. However, when the position of the robot is not fixed and the pose of the working object is unknown, the robot needs to obtain the pose of the working object and the relative position relationship between the robot and the working object to grasp the target. Aiming at the above problems, this paper designs a pose estimation method for the manipulator to grasp the target. Firstly, the backbone network ResNet50 is replaced by a lightweight, efficient and less computationally intensive MobileNet network to improve the Mask R-CNN network. Secondly, the improved neural network was trained by transfer learning based on image acquisition and COCO dataset. Finally, the pose estimation method of binocular data source based on line features is designed to obtain the pose information of grasping objects. Experimental results show that the operating speed of MobileNet Mask R-CNN network is faster than Mask R-CNN network, and the speed of target recognition is also increased by about 4%. At the same time, high precision pose information can be obtained, and it has high efficiency and robustness." @default.
- W4382246145 created "2023-06-28" @default.
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- W4382246145 date "2023-06-27" @default.
- W4382246145 modified "2023-09-24" @default.
- W4382246145 title "Robot arm gripping pose estimation algorithm based on binocular camera" @default.
- W4382246145 doi "https://doi.org/10.1117/12.2680144" @default.
- W4382246145 hasPublicationYear "2023" @default.
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