Qingsong Fan, Qijie Rao, Haisong Huang*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1825-1848, 2023, DOI:10.32604/cmes.2023.028369
Abstract In present-day industrial settings, where robot arms perform tasks in an unstructured environment, there may exist
numerous objects of various shapes scattered in random positions, making it challenging for a robot arm to precisely
attain the ideal pose to grasp the object. To solve this problem, a multistage robotic arm flexible grasp detection
method based on deep learning is proposed. This method first improves the Faster RCNN target detection model,
which significantly improves the detection ability of the model for multiscale grasped objects in unstructured
scenes. Then, a Squeeze-and-Excitation module is introduced to design a multitarget grasping pose generation
network… More >
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