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- W4387299743 abstract "To solve the problems such as the difficulty in grasping detection and inaccurate positioning of manipulators in an unstructured environment, this paper proposes a manipulator grasping detection method based on a lightweight feature fusion convolution neural network, which is used to predict the grasping position and orientation of target objects. This method takes the three-channel RGB image and single-channel depth map as the network input, constructs the one-to-one mapping relationship between the object and the pixel, effectively avoids time-consuming operations such as discrete sampling and candidate sorting, and adopts the multi-constraint prediction result evaluation method to improve the accuracy of capture detection. The experimental results show that the method in this paper can accurately predict the grasping position and posture in an unstructured environment. The success rate of grasping is 86.7%, and the processing speed of a single image is about 18ms. The balance between precision and speed is achieved, which has reference value for solving the problem of difficult robot autonomous grasping in the unstructured environment." @default.
- W4387299743 created "2023-10-04" @default.
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- W4387299743 date "2023-02-01" @default.
- W4387299743 modified "2023-10-06" @default.
- W4387299743 title "Robotic Grasp Detection Method Based on Lightweight Feature Fusion Convolutional Neural Network" @default.
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- W4387299743 doi "https://doi.org/10.1109/iscer58777.2023.00016" @default.
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