Multitarget Flexible Grasping Detection Method for Robots in Unstructured Environments  

作　　者：Qingsong Fan Qijie Rao Haisong Huang 

机构地区：[1]Key Laboratory of Modern Manufacturing Technology,Ministry of Education,Guizhou University,Guiyang,550025,China

出　　处：《Computer Modeling in Engineering & Sciences》2023年第11期1825-1848,共24页工程与科学中的计算机建模（英文）

基　　金：supported in part by the National Natural Science Foundation of China(No.52165063);Guizhou Provincial Science and Technology Projects(Qiankehepingtai-GCC[2022]006-1,Qiankehezhicheng[2021]172,[2021]397,[2021]445,[2022]008,[2022]165);Natural Science Research Project of Guizhou Provincial Department of Education(Qianjiaoji[2022]No.436);Guizhou Province Graduate Research Fund(YJSCXJH[2021]068).

摘　　要：In present-day industrial settings,where robot arms performtasks in an unstructured environment,theremay exist numerousobjects of various shapes scattered in randompositions,making it challenging for a robot armtoprecisely 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.

关 键 词：Unstructured scene ROBOT target detection grab pose detection deep learning 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]