Research on Robot Motion Control and Visual Grasping Based on Improved Faster RCNN
Target visual recognition is the key of intelligent robot to perform high-precision target grasping operation and one of the key problems affecting the industrial application of intelligent robots.To improve the motion control effect and object grasping accuracy of object grasping robot,the robot motion control and object visual grasping are studied,and a new method of robot mo-tion control and object visual recognition based on the improved Faster RCNN is proposed in this research.To ensure the target rec-ognition and grasping precision of intelligent robot,the zero position of robot body is calibrated by the least square method.The convolution layer and pooling layer of Faster RCNN are improved by using multi-label images of open data sets to improve the ac-curacy and efficiency of target image detection and recognition.Through grasping algorithm based on multi-objective and multi-class detection deep learning framework,the motion control modeling and solving of robot arm are realized,and the motion con-trol effect of object grasping robot is improved.For verifying the effectiveness of the proposed method based on improved Faster RCNN for robot motion control and object visual recognition,visual recognition and grasping experiments in real environment are carried out.The experimental results show that the new method of robot motion control and object visual recognition based on im-proved Faster RCNN can greatly improve the accuracy of object recognition and effectively improve the efficiency of robot grasping object.
Visual Recognition and Grasping of TargetsRobot Zero CalibrationMotion ControlDeep Learning
NETL
NSTL
万方数据
