Shiyin Qiu1,*, David Lodder2, Feifan Du2
Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1517-1529, 2020, DOI:10.32604/iasc.2020.012144
- 24 December 2020
Abstract Robotic grasping is an important issue in the field of robot control. In order to solve the problem of optimal grasping pose of the robotic arm, based on the Generative Grasping Convolutional Neural Network (GG-CNN), a new convolutional neural network called Hybrid Generative Grasping Convolutional Neural Network (HGG-CNN) is proposed by combining three small network structures called Inception Block, Dense Block and SELayer. This new type of convolutional neural network structure can improve the accuracy rate of grasping pose based on the GG-CNN network, thereby improving the success rate of grasping. In addition, the HGG-CNN More >
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