@Article{cmes.2023.028369,
AUTHOR = {Qingsong Fan, Qijie Rao, Haisong Huang},
TITLE = {Multitarget Flexible Grasping Detection Method for Robots in Unstructured Environments},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {2},
PAGES = {1825--1848},
URL = {http://www.techscience.com/CMES/v137n2/53342},
ISSN = {1526-1506},
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 based on a deep convolutional neural network to generate a variety of graspable poses for grasped objects.
Finally, a multiobjective IOU mixed area attitude evaluation algorithm is constructed to screen out the optimal
grasping area of the grasped object and obtain the optimal grasping posture of the robotic arm. The experimental
results show that the accuracy of the target detection network improved by the method proposed in this paper
reaches 96.6%, the grasping frame accuracy of the grasping pose generation network reaches 94% and the flexible
grasping task of the robotic arm in an unstructured scene in a real environment can be efficiently and accurately
implemented.},
DOI = {10.32604/cmes.2023.028369}
}