Traditional steel pipe counting can only be performed manually, which has the problems of large workload, error prone and low efficiency. This paper proposes a steel pipe counting system based on image recognition. First of all, the steel pipe image is collected from the camera, and then the digital image processing technology is used to perform image enhancement, edge detection and morphological operation and other preprocessing, then the steel pipe is identified and counted by the Hough circle transformation, and finally the steel pipe automatic is developed using VC++ software Technical software. Experiments show that the automatic counting accuracy and efficiency of steel pipes in this system are high.
In this paper, we propose an economical system for remote video player control. Through this system, we can use several simple gestures to control the video player, and these gestures can be alternated based on the user’s requirement and habits. The datasets used to train the gesture recognition model are recorded by a simple web camera in the laboratory. We utilize the CNN (convolutional neural network) to train the datasets and the user interface is designed by PyQt5. The gesture recognition system can be applied to switching television programs, controlling video games and household appliances etc.
With the development of collaborative robots, robot programming by demonstration (PBD) plays an important role in human-robot interaction, it aims to transfer new skills from observations of tasks demonstrated by humans to robots. In this paper, we proposed a new approach to teach a robot to draw pictures based on human fingertip recognition and hand motion tracking. Combining the robot operating system (ROS), OpenCV and Moveit (motion planning libraries), we capture the finger movement trajectory by using Kinect2 depth camera. Then the trajectory waypoint is sent to the ur5 robotic arm through topic communication to complete the trajectory tracking task. The experiment indicates that the proposed approach allows inexperienced users to efficiently teach a robot to track the demonstrated trajectory.
KEYWORDS: Operating systems, Laser processing, Data conversion, Mobile robots, Particles, Transform theory, Laser range finders, Data processing, Distance measurement, Data acquisition
Laser SLAM can be implemented using ROS and Ubuntu system. However, it cannot be run in Windows operating system which is more stable than Ubuntu. To implement the laser SLAM in Windows system, the main program of laser SLAM in ROS is carefully analyzed and modified to make it adapt to Windows system. The main programs of laser processing, coordinate transformation and map construction are rewritten and reorganized. To verify the effectiveness of our work, experiments were conducted in real-world environments. The results of experiments validated that laser SLAM can be implemented in Windows system by rewriting and reorganizing these main programs.
Machine learning has made breakthroughs in areas such as computer vision and natural language processing. In recent years, more and more research has been done on the application of machine learning on robotic grasping. This article summarizes the research progress of machine learning on robotic grasping, from the aspects of object grasping datasets, two main categories of methods that differ from the criteria for successful grasping with deep learning or reinforcement learning algorithm, discusses what current researches have done and the problems that have not yet been solved, and hopes to inspire new ideas in research of robotic grasping based on machine learning.
Hand gesture recognition (HGR) is a natural way of Human Machine Interaction and has been applied on different areas. In this paper, we discuss works done in the area of applications of HGR in industrial robots where focus is on the processing steps and techniques in gesture-based Human Robot Interaction (HRI), which can provide useful information for other researchers. We review several related works in the area of HGR based on different approaches including sensor based approach and vision approach. After comparing the two approaches, we found that 3D vision-based HGR method is a challenging but promising researching area. Then, concerning works of implementation of HGR in industrial scenario are discussed in detail. Pattern recognition algorithms that effectively used in HGR like k-means, DTW etc. are briefly introduced as well.
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