Object Recognition And Tracking Of NAO Robot Based On Deep Learning

With the rapid development of deep learning algorithms,a major breakthrough has been made in the field of image target recognition.Both the determination of target categories and the detection of target locations have achieved much higher accuracy than traditional machine learning algorithms.Robotics is an interdisciplinary branch of engineering and science,including mechanical engineering,information engineering,and computer science.Robotics involves the design and manufacture of robots and computer systems for controlling robot motion,sensor feedback,and information processing.Robot vision is a research hotspot in robotics.It involves how to use robots to collect visual information of the environment and obtain high-level understanding of digital images or videos.From an engineering perspective,it attempts to automate tasks that the human visual system can accomplish.The deep learning algorithm applied to the field of robot vision will greatly improve the interactive ability of the robot's environment and improve the intelligence of the robot.In this paper,the NAO robot is used as a research platform for robot vision technology.The method of target recognition and tracking based on deep learning algorithm is mainly studied.The specific work of this article is as follows:(1)Studied the basic principle of deep learning algorithm,deeply studied the Faster R-CNN target recognition model,improved the feature extraction network of Faster R-CNN,and designed a 98-layer volume based on the residual network model.Neural network to extract image features.The Faster R-CNN target recognition model is optimized for small target recognition.Build a training platform on Google Cloud,use PASCAL VOC as a training data set,train the improved Faster R-CNN target recognition model,and obtain higher accuracy than the original model.(2)In order to achieve the NAO robot tracking target,the NAO robot camera ranging was studied.Although the NAO robot has two cameras up and down,but the two sights do not intersect and can not run simultaneously.Therefore,this project constructs the NAO robot monocular ranging model ranging.The NAO robot tracking target is set to the horizontal plane.Therefore,a ground plane constraint ranging model is established.Experiments show that the NAO robot achieves a high accuracy in the monocular range finding.(3)For NAO robot hardware platform,Choregraphe software was used to establish a C/S NAO robot target recognition and tracking system,and the deep learning target recognition algorithm module and NAO robot monocular distance measurement module were implemented on the server.Package,NAO robots call packaged algorithm modules from the server via the HTTP protocol.Experimental results show that NAO robot target recognition and tracking achieve higher accuracy.