scholarly journals Intelligent Lifebuoy Based on Machine Vision

2021 ◽  
Vol 2078 (1) ◽  
pp. 012055
Author(s):  
Lintao Hu
Keyword(s):  
Machine Vision ◽  
Differential Rotation ◽  
Motor Drive ◽  
Position Information

Abstract This article mainly introduces the intelligent lifebuoy based on machine vision. The design uses the OpenMv camera to identify the drowning person. OpenMV sends the collected position information of the person to the MCU. If the drowning person is on the left or right side of the lifebuoy, the MCU controls the motor drive module. Control the differential rotation of the motor behind the lifebuoy so that the lifebuoy is facing the drowning person. If the drowning person is directly in front of the lifebuoy, the MCU controls the motor drive module to control the rotation of the motor behind the lifebuoy to make the lifebuoy gradually approach the drowning person, which can save life. Blue balloons was used instead of drowning people to conduct rescue experiments on the lake. The smart lifebuoy successfully recognized the blue balloon and swam near the blue balloon. Furthermore, a perfect solution is proposed for the shortcomings of the intelligent lifebuoy.

scholarly journals Target Tracking Algorithm for Table Tennis Using Machine Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Hongtu Zhao ◽  
Fu Hao
Keyword(s):  
Neural Network ◽  
Machine Vision ◽  
Deep Neural Network ◽  
Tracking Algorithm ◽  
Tennis Ball ◽  
Table Tennis ◽  
Ball Speed ◽  
Position Information ◽  
Offline Learning ◽  
Hidden Layer

The current table tennis robot system has two common problems. One is the table tennis ball speed, which moves fast, and it is difficult for the robot to react in a short time. The second is that the robot cannot recognize the type of the ball's movement, i.e., rotation, top rotation, no rotation, wait, etc. It is impossible to judge whether the ball is rotating and the direction of rotation, resulting in a single return strategy of the robot with poor adaptability. In this paper, these problems are solved by proposing a target trajectory tracking algorithm for table tennis using machine vision combined with Scaled Conjugate Gradient (SCG). Real human-machine game’s data are obtained in the proposed algorithm by extracting ten continuous position information and speed information frames for feature selection. These features are used as input data for the deep neural network and then are normalized to create a deep neural network algorithm model. The model is trained by the position information of the successive 20 frames. During the initial sets of experiments, we found the shortcomings of the original SCG algorithm. By setting the accuracy threshold and offline learning of historical data and saving the hidden layer weight matrix, the SCG algorithm was improved. Finally, experiments verify the improved algorithm's feasibility and applicability and show that the proposed algorithm is more suitable for table tennis robots.

Sensorless Control Based on Virtual Neutral Voltage for BLDC Motor Drive

2015 ◽  
Vol 789-790 ◽  
pp. 962-966
Author(s):  
Chih Hsien Yu ◽  
Chyuan Yow Tseng
Keyword(s):  
Phase Shifter ◽  
Motor Drives ◽  
Motor Drive ◽  
Main Theme ◽  
Bldc Motor ◽  
Cross Point ◽  
Position Information ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

Based on the characteristics of the back electromotive force (back-EMF), the rotor position information would be detected. Hence, the main theme of this paper is to design a practical approach to detect the zero cross point (ZCP) of back-EMF through virtual neutral voltage for sensorless brushless direct current (BLDC) motor drives. In contrast to conventional methods, the real neutral voltage of motor is not needed. In order to compensate the phase delay of the back-EMF due to low-pass filter (LPF) under different speeds, a voltage-controlled phase shifter, consisted of hysteresis comparator and voltage-controlled resistor (VCR), is proposed in this paper. The detail circuit model is introduced and some experimental results obtained from a sensorless prototype are shown to confirm the practicality of proposed senosorless drive method.

scholarly journals Measurement of Natural Properties of a Beam Based on Machine Vision

2011 ◽  
Vol 2-3 ◽  
pp. 162-166
Author(s):  
Qing Peng Han ◽  
Pei Xin Gao ◽  
Ling Tang ◽  
Xiao Wei Sun ◽  
Zhong Luo
Keyword(s):  
Machine Vision ◽  
Marked Point ◽  
Measurement Methods ◽  
Processing Method ◽  
Flexible Beam ◽  
Test Results ◽  
Centroid Method ◽  
Position Information ◽  
Natural Properties ◽  
Previous Contact

The natural properties of flexible beam is measured by using machine vision in which the image of vibrate flexible Beam is acquired continuously. Different from the previous contact measurement methods, the binary processing method and centroid method is utilized to binarize the image and extracts the position of marked point. Gathering and calculating the position information preprocessed in MATLAB indicates the natural properties of flexible beam. Furthermore, the test results compared with the theoretical analysis verifies the validity of the proposed method.

Measurement of Positioning Barrel Eccentric Hole Based on Machine Vision Technology

2011 ◽  
Vol 143-144 ◽  
pp. 726-730
Author(s):  
Xue Feng Wu ◽  
Yu Fan
Keyword(s):  
Edge Detection ◽  
Drive System ◽  
Motor Drive ◽  
Image Process ◽  
Stepping Motor ◽  
Two Phase ◽  
Position Information ◽  
Detection Technology ◽  
Actual Space ◽  
Motor Drive System

A measurement based on image process is proposed for detecting the hole on a barrel. The barrel image is separated from background with the image edge detection technology. The different detection operators are compared and the sobel operator is used for edge detection. The actual space position of the hole on barrel is computed by circle detecting method. Gradient hough transform is used for detecting circle position information, including circle radius and two dimension coordinates. The stepping motor drive system is consists of a control card, stepping motor drive units and two two-phase hybrid stepping motors. Automatic positioning is achieved through stepping motor drive system.

scholarly journals Multi-motor Drive Control Method of Upper-Retort-Robot Based on Machine Vision

2021 ◽  
Author(s):  
Jie Xiao ◽  
Wanjie Kang ◽  
Guofeng He ◽  
Xiangchen Li ◽  
Genglong Yan
Keyword(s):  
Machine Vision ◽  
Control Method ◽  
Processing System ◽  
The Body ◽  
Motor Drive ◽  
Transmission Performance ◽  
Cold Regions ◽  
Field Equipment ◽  
Drive Control

Abstract A research on the multi-motor drive control method of the upper-retort-robot based on machine vision is proposed in this paper for wine brewing automation to suffice the demand of military areas situated in cold regions as wine is recommended to keep the body temperature of soldiers normal in highly cold regions of China. Based on machine vision, the target is converted into an image signal by an image pickup device and is sent to the image processing system. The pixel distribution, brightness, color and other information are converted into digital signals and the target features are extracted to control the actions of the field equipment. The Monte-Carlo method is exploited to randomly generate joint variables within the variation range of each joint. The positive aspects of kinematics model are utilized and the working space of the upper-retort-robot is calculated using multi-motor drive control method. The multi-motor drive compensates the harmonic ripple torque, and establishes the fault-tolerant automatic control of the system to maintain quality of the liquor. The experimental results show that the robot arm can reach at any position in the barrel within the defined range. The robot will work in an automated mode to control the quality of the liquor. The transmission performance of the robot can meet the requirements of the automated quality control of the liquor during processing of wine from grapes. The results are obtained for robot transmission performance and robot dexterity which proves the robustness and viability of the proposed multi-motor drive control method (MMDCM).

scholarly journals Machine vision-based driving and feedback scheme for digital microfluidics system

2021 ◽  
Vol 19 (1) ◽  
pp. 665-677
Author(s):  
Zhijie Luo ◽  
Bangrui Huang ◽  
Jiazhi Xu ◽  
Lu Wang ◽  
Zitao Huang ◽  
...  
Keyword(s):  
Machine Vision ◽  
New Technology ◽  
Digital Microfluidics ◽  
Microfluidic System ◽  
Position Information ◽  
Electrowetting On Dielectric ◽  
System A ◽  
Feedback Scheme ◽  
Voltage Signal ◽  
Digital Microfluidic

Abstract A digital microfluidic system based on electrowetting-on-dielectric is a new technology for controlling microliter-sized droplets on a plane. By applying a voltage signal to an electrode, the droplets can be controlled to move, merge, and split. Due to device design, fabrication, and runtime uncertainties, feedback control schemes are necessary to ensure the reliability and accuracy of a digital microfluidic system for practical application. The premise of feedback is to obtain accurate droplet position information. Therefore, there is a strong need to develop a digital microfluidics system integrated with driving, position, and feedback functions for different areas of study. In this article, we propose a driving and feedback scheme based on machine vision for the digital microfluidics system. A series of experiments including droplet motion, merging, status detection, and self-adaption are performed to evaluate the feasibility and the reliability of the proposed scheme. The experimental results show that the proposed scheme can accurately locate multiple droplets and improve the success rate of different applications. Furthermore, the proposed scheme provides an experimental platform for scientists who focused on the digital microfluidics system.

Machine Vision

2009 ◽  
Author(s):  
Wesley E. Snyder ◽  
Hairong Qi
Keyword(s):  
Machine Vision

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

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