scholarly journals Line Tracing Technique for Smooth Driving

2019 ◽  
Vol 9 (1) ◽  
pp. 4535-4540
Keyword(s):  
Raspberry Pi ◽  
Steering Angle ◽  
Black Line ◽  
Line Tracking ◽  
Tracing Algorithm

In this paper, a line tracing algorithm for a robot was developed. The line tracing algorithm uses the robot’s car attached camera to detect the black line. The steering angle of the robot car’s wheels is then adjusted according to the black line detected. The performance of the proposed algorithm was evaluated by using the simple line tracking. We found out that using this technique the robot car is having a smooth drive following the black line. This study involves both hardware and software: raspberry pi as the microcontroller for the hardware and C++ for the software.

Lessons from adopting a maker approach to teaching operating systems with Raspberry Pi

2018 ◽  
Vol 15 (2) ◽  
pp. 119-131
Author(s):  
Wing-Kwong Wong
Keyword(s):  
Operating Systems ◽  
Design Methodology ◽  
Raspberry Pi ◽  
Maker Movement ◽  
Black Line ◽  
Content Type ◽  
Hands On ◽  
Approach To Teaching ◽  
Practical Implications ◽  
The Maker Movement

Purpose This paper aims to propose a maker’s approach to teaching an operating systems (OSs) course in which students apply knowledge of OSs to making a toy robot by focusing on input/outputs, hardware devices and system programming. Design/methodology/approach Classroom action research is involved in this study. Findings After the course was taught in this maker’s approach in two consecutive school years, some observations were reported. Students were enthusiastic in doing a series of assignments leading to the completion of a toy robot that follows a black line on the ground. In addition to enjoying the learning process by making tangible products, the students were excited to be able to demonstrate the skills and knowledge they learned with the robots they made. Research limitations/implications The research results were based mainly on the instructor’s observations during the lectures and labs. Practical implications Lessons from this study can inspire other instructors to turn traditional engineering courses into maker courses to attract students who enjoy making. Industry should welcome engineering graduates to join the companies with more hands-on experiences they have gained from maker courses. Social implications Although the maker movement has attracted much attention in K12 education, there is little research that studies how this maker spirit can be incorporated in traditional engineering courses that focus mainly on theories or software. Originality/value Including electronics and mechanical components in programming assignments would bring surprising effects on students’ motivation in learning.

scholarly journals Comparative Analysis of Steering Angle Prediction For Automated Object Using Deep Neural Network

2021 ◽  
Author(s):  
Md Khairul Islam ◽  
Mst. Nilufa Yeasmin ◽  
Chetna Kaushal ◽  
Md Al Amin ◽  
Md Rakibul Islam ◽  
...  
Keyword(s):  
Deep Neural Network ◽  
Mean Squared Error ◽  
Comparative Investigation ◽  
Training Data ◽  
Raspberry Pi ◽  
Processing Unit ◽  
Steering Control ◽  
Steering Angle ◽  
Squared Error ◽  
Entire Planet

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia model outperforms the other pre-trained models, even though other models work well.<br>

scholarly journals Comparative Analysis of Steering Angle Prediction For Automated Object Using Deep Neural Network

2021 ◽  
Author(s):  
Md Khairul Islam ◽  
Mst. Nilufa Yeasmin ◽  
Chetna Kaushal ◽  
Md Al Amin ◽  
Md Rakibul Islam ◽  
...  
Keyword(s):  
Deep Neural Network ◽  
Mean Squared Error ◽  
Comparative Investigation ◽  
Training Data ◽  
Raspberry Pi ◽  
Processing Unit ◽  
Steering Control ◽  
Steering Angle ◽  
Squared Error ◽  
Entire Planet

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia model outperforms the other pre-trained models, even though other models work well.<br>

scholarly journals Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 126
Author(s):  
Md. Kalim Amzad Chy ◽  
Abdul Kadar Muhammad Masum ◽  
Kazi Abdullah Mohammad Sayeed ◽  
Md Zia Uddin
Keyword(s):  
Deep Learning ◽  
Power Supply ◽  
Geographical Location ◽  
Buck Converter ◽  
Rapid Expansion ◽  
Raspberry Pi ◽  
Delivery Vehicle ◽  
Steering Angle ◽  
The Road ◽  
Product Delivery

The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system’s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system’s infrastructure is far too low-cost and easy to install.

scholarly journals Comparative Analysis of Steering Angle Prediction For Automated Object Using Deep Neural Network

2021 ◽  
Author(s):  
Md Khairul Islam ◽  
Mst. Nilufa Yeasmin ◽  
Chetna Kaushal ◽  
Md Al Amin ◽  
Md Rakibul Islam ◽  
...  
Keyword(s):  
Deep Neural Network ◽  
Mean Squared Error ◽  
Comparative Investigation ◽  
Training Data ◽  
Raspberry Pi ◽  
Processing Unit ◽  
Steering Control ◽  
Steering Angle ◽  
Squared Error ◽  
Entire Planet

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia model outperforms the other pre-trained models, even though other models work well.<br>

PROTOTYPE ALAT PEMANTAU SALURAN AIR BAWAH TANAH DENGAN MENGGUNAKAN WEBCAM C170 BERBASIS RASPBERRY PI

2015 ◽  
Vol 1 (1) ◽  
pp. 37-45
Author(s):  
Irwansyah Irwansyah ◽  
Hendra Kusumah ◽  
Muhammad Syarif
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Public Awareness ◽  
Underground Water ◽  
Raspberry Pi ◽  
Laptop Computer ◽  
Web Browser ◽  
Disaster Monitoring ◽  
The Times ◽  
Human Desire

Along with the times, recently there have been found tool to facilitate human’s work. Electronics is one of technology to facilitate human’s work. One of human desire is being safe, so that people think to make a tool which can monitor the surrounding condition without being monitored with people’s own eyes. Public awareness of the underground water channels currently felt still very little so frequent floods. To avoid the flood disaster monitoring needs to be done to underground water channels.This tool is controlled via a web browser. for the components used in this monitoring system is the Raspberry Pi technology where the system can take pictures in real time with the help of Logitech C170 webcam camera. web browser and Raspberry Pi make everyone can control the devices around with using smartphone, laptop, computer and ipad. This research is expected to be able to help the users in knowing the blockage on water flow and monitored around in realtime.

Implementasi Voip Server Berbasis IPV6 Dengan Raspberry PI

2019 ◽  
Vol 9 (01) ◽  
pp. 47-54
Author(s):  
Rabbai San Arif ◽  
Yuli Fitrisia ◽  
Agus Urip Ari Wibowo
Keyword(s):  
Internet Protocol ◽  
Wireless Transmission ◽  
Raspberry Pi ◽  
Test Results ◽  
Average Delay ◽  
Ipv6 Network ◽  
Ip Network ◽  
Selection Of ◽  
Better Than

Voice over Internet Protocol (VoIP) is a telecommunications technology that is able to pass the communication service in Internet Protocol networks so as to allow communicating between users in an IP network. However VoIP technology still has weakness in the Quality of Service (QoS). VOPI weaknesses is affected by the selection of the physical servers used. In this research, VoIP is configured on Linux operating system with Asterisk as VoIP application server and integrated on a Raspberry Pi by using wired and wireless network as the transmission medium. Because of depletion of IPv4 capacity that can be used on the network, it needs to be applied to VoIP system using the IPv6 network protocol with supports devices. The test results by using a wired transmission medium that has obtained are the average delay is 117.851 ms, jitter is 5.796 ms, packet loss is 0.38%, throughput is 962.861 kbps, 8.33% of CPU usage and 59.33% of memory usage. The analysis shows that the wired transmission media is better than the wireless transmission media and wireless-wired.

Effect of Inner and Outer Wheels Driving Force Control on Small Electric Vehicle

2020 ◽  
Vol 17 (4) ◽  
pp. 8246-8254
Author(s):  
K. Shibazaki ◽  
H. Nozaki
Keyword(s):  
Control System ◽  
Angular Velocity ◽  
Electric Vehicle ◽  
Force Constant ◽  
Force Control ◽  
Driving Force ◽  
Vehicle Control ◽  
Steering Angle ◽  
Force Control System ◽  
The Difference

In this study, in order to improve steering stability during turning, we devised an inner and outer wheel driving force control system that is based on the steering angle and steering angular velocity, and verified its effectiveness via running tests. In the driving force control system based on steering angle, the inner wheel driving force is weakened in proportion to the steering angle during a turn, and the difference in driving force is applied to the inner and outer wheels by strengthening the outer wheel driving force. In the driving force control (based on steering angular velocity), the value obtained by multiplying the driving force constant and the steering angular velocity,  that differentiates the driver steering input during turning output as the driving force of the inner and outer wheels. By controlling the driving force of the inner and outer wheels, it reduces the maximum steering angle by 40 deg and it became possible to improve the cornering marginal performance and improve the steering stability at the J-turn. In the pylon slalom it reduces the maximum steering angle by 45 deg and it became possible to improve the responsiveness of the vehicle. Control by steering angle is effective during steady turning, while control by steering angular velocity is effective during sharp turning. The inner and outer wheel driving force control are expected to further improve steering stability.

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