Moving Object Prediction and Grasping System of Robot Manipulator
學年 110
學期 2
出版（發表）日期 2022-02-15
作品名稱 Moving Object Prediction and Grasping System of Robot Manipulator
作品名稱（其他語言）
著者 Ching-Chang Wong; Ming-Yi Chien; Ren-Jie Chen; Hisayuki Aoyama; Kai-Yi Wong
單位
出版者
著錄名稱、卷期、頁數 IEEE Access 10, p.20159-20172
摘要 In this paper, we designed and implemented a moving object prediction and grasping system that enables a robot manipulator using a two-finger gripper to grasp moving objects on a conveyor and a circular rotating platform. There are three main parts: (i) moving object recognition, (ii) moving object prediction, and (iii) system realization and verification. In the moving object recognition, we used the instance segmentation algorithm of You Only Look At CoefficienTs (YOLACT) to recognize moving objects. The recognition speed of YOLACT can reach more than 30 fps, which is very suitable for dynamic object recognition. In addition, we designed an object numbering system based on object matching, so that the system can track the target object correctly. In the moving object prediction, we first designed a moving position prediction network based on Long Short-Term Memory (LSTM) and a grasping point prediction network based on Convolutional Neural Network (CNN). Then we combined these two networks and designed two moving object prediction networks, so that they can simultaneously predict the grasping positions and grasping angles of multiple moving objects based on image information. In the system realization and verification, we used Robot Operating System (ROS) to effectively integrate all the programs of the proposed system for the camera image extraction, strategy processing, and robot manipulator and gripper control. A laboratory-made conveyor and a circular rotating platform and four different objects were used to verify that the implemented system could indeed allow the gripper to successfully grasp moving objects on these two different object moving platforms.
關鍵字 Moving object prediction;object grasping;long short-term memory (LSTM);convolutional neural network (CNN);you only look at the coefficients (YOLACT)
語言 en_US
ISSN 2169-3536
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 USA
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/122728 )

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