scholarly journals An Innovative Social Mobile Platform to Support Real Time Communication in Peer Tutoring

2017 ◽  
Author(s):  
Meghan Wang ◽  
Yu Sun
Keyword(s):  
Real Time ◽  
Peer Tutoring ◽  
Mobile Platform

Robotic System Used for Navigation and Tracking, in Real Time, with the Obstacle Avoidance

2014 ◽  
Vol 1077 ◽  
pp. 221-226
Author(s):  
Dan Popescu ◽  
Loretta Ichim ◽  
Radu Fratila ◽  
Diana Gornea
Keyword(s):  
Real Time ◽  
Obstacle Avoidance ◽  
Robotic System ◽  
Indoor Navigation ◽  
Object Localization ◽  
Mobile Platform ◽  
Mobile Object ◽  
Central Unit ◽  
The Road ◽  
Localization And Tracking

Tracking the road or a mobile object and also obstacle avoidance are very important components that must be considered in the process of developing a robotic system. In this paper we propose a mobile platform for indoor navigation, based on a cheap computing hardware, which is able to be configured in two scenarios: the first refers to the movement of the robot on a predetermined path and to avoidance the obstacles, while maintaining the final target, and the second refers to the possibility of identifying and tracking a target. The robotic system aggregates the information acquired from different sensors and combines the computing resources from the mobile platform with those from the central unit. MATLAB is used for all the implementations and tests, to develop algorithms and to create models and applications. The robot's communication with central unit is wireless. Experimental results show that the mobile platform is able to perform, in real time, the following tasks in indoor environment: the recognition of the object, localization and tracking and also the obstacles avoidance.

REAL-TIME SLIDING-MODE ADAPTIVE CONTROL OF A MOBILE PLATFORM WHEELED MOBILE ROBOT

2008 ◽  
Vol 41 (2) ◽  
pp. 4393-4399 ◽  
Author(s):  
A. Filipescu ◽  
AL. Stancu ◽  
S. Filipescu ◽  
G. Stamatescu
Keyword(s):  
Adaptive Control ◽  
Mobile Robot ◽  
Real Time ◽  
Sliding Mode ◽  
Wheeled Mobile Robot ◽  
Mobile Platform

scholarly journals Real Time Object Detection & Tracking over a Mobile Platform

2016 ◽  
Vol 8 (32) ◽  
Author(s):  
M. A. Mekhtiche ◽  
M. A. Bencherif ◽  
M. Algabri ◽  
M. Alsulaiman ◽  
R. Hedjar ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Mobile Platform

Methods and means for real-time object recognition accuracy increase in video images on ios mobile platform

2021 ◽  
Vol 3 (1) ◽  
pp. 80-88
Author(s):  
D Kushnir ◽  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Recognition Accuracy ◽  
Activation Function ◽  
Frame Rate ◽  
Mobile Platform ◽  
Additional Layer ◽  
The Neural Network ◽  
Analytical Review ◽  
Accuracy Increase

As a result of the analytical review, it was established that the family of Yolo models is a promising area of search and recognition of objects. However, existing implementations do not support the ability to run the model on the iOS platform. To achieve these goals, a comprehensive scalable conversion system has been developed to improve the recognition accuracy of arbitrary models based on the Docker system. The method of improvement is to add a layer with the Mish activation function to the original model. The method of conversion is to quickly convert any Yolo model to CoreML format. As part of the study of these techniques, a model of the neural network Yolov4_TCAR was created. Additionally, a method of accelerating the load on the CPU using an additional layer of neural network with the function of activating Mish in Swift for the iOS mobile platform was added. As a result, the effectiveness of the Mish activation function, the CPU load of the mobile device, the amount of RAM used, and the frame rate when using the improved original Yolov4-TCAR model were studied. The results of the research confirmed the functioning of the algorithm for conversion and accuracy increase of the neural network model in real-time.

PID control of an omnidirectional mobile platform based on an RBF neural network controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Niu Zijie ◽  
Zhang Peng ◽  
Yongjie Cui ◽  
Zhang Jun
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Control Algorithm ◽  
Rbf Neural Network ◽  
Mobile Platform ◽  
Content Type ◽  
The Neural Network ◽  
Omnidirectional Mobile Platform

Purpose Omnidirectional mobile platforms are still plagued by the problem of heading deviation. In four-Mecanum-wheel systems, this problem arises from the phenomena of dynamic imbalance and slip of the Mecanum wheels while driving. The purpose of this paper is to analyze the mechanism of omnidirectional motion using Mecanum wheels, with the aim of enhancing the heading precision. A proportional-integral-derivative (PID) setting control algorithm based on a radial basis function (RBF) neural network model is introduced. Design/methodology/approach In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. Findings The network RBF NN1 calculates the deviations ?Kp, ?Ki and ?Kd to regulate the three coefficients Kp, Ki and Kd of the heading angle PID controller. This corrects the driving heading in real time, resolving the problems of low heading precision and unstable driving. The experimental data indicate that, for a externally imposed deviation in the heading angle of between 34º and ∼38°, the correction time for an omnidirectional mobile platform applying the algorithm during longitudinal driving is reduced by 1.4 s compared with the traditional PID control algorithm, while the overshoot angle is reduced by 7.4°; for lateral driving, the correction time is reduced by 1.4 s and the overshoot angle is reduced by 4.2°. Originality/value In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. The method is innovative.

Real-time illumination and shadow invariant lane detection on mobile platform

2017 ◽  
Vol 16 (5) ◽  
pp. 1781-1794 ◽  
Author(s):  
Ayhan Küçükmanisa ◽  
Gökhan Tarım ◽  
Oğuzhan Urhan
Keyword(s):  
Real Time ◽  
Lane Detection ◽  
Mobile Platform
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