kishan-vk/IR-SENSOR-BASED-AUTONOMOUS-ROBOT-LINE-FOLLOWER kishan-vk/IR-SENSOR-BASED-AUTONOMOUS-ROBOT-LINE-FOLLOWER

  • Stars
    star
    26
  • Rank 927,650 (Top 19 %)
  • Language
  • Created about 4 years ago
  • Updated about 4 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
kishan-vk/IR-SENSOR-BASED-AUTONOMOUS-ROBOT-LINE-FOLLOWER
github

kishan-vk

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Here, we are using the two ir sensors to detect the objects infront of them means it reads(digital reads) and there outputs are taken as a input to arduino. And take the jumper wires connect between the L293D(driver) and arduino uno. Take one 12v battery and connect it to the driver. But in ir sensor consist of three terminals, there are Vcc,gnd and o/p. And for ir sensor it need 5v source and it can be taken from the 12v battery through voltage regulator(7805). In motor driver it consist of left motor and right motor. Through help of coding, we can control the wheels of robot(line follower). And it consist of two positive terminals and two negative terminals. Here (+) terminal indicates the forward direction and (-) terminal indicates the backward direction. AndTo move the robot in forward direction, we must give lm(+) and rm(+) is high. And you want to take the Right direction we must code like give rm1(+)and rm2(+) as a low(0) and lm1 is high and lm2 is low then right direction. And if you want to take the left direction then lm1and2 are given as low and lm1 is taken as high and m3 is taken as low then it takes the left direction. And if you want to stop the robot then give the low to lm and rm motors(rm-right motor; lm- left motor). And the work of robot totally depends on the control statements.

More Repositories

1

Automatic-Blood-Type-Detection-using-image-processing

Determining of blood types is very important during emergency situation before administering a blood transfusion.presently,these tests are performed manually by technicians, which can lead to human errors. A method is developed based on processing of images acquired during the slide test.The image processing techniques such as Pre-processing, Segmentation, Thresholding, Morphological operatios and support vector machines are used. The images of the slide test are obtained from pathological laboratory are processed and the occurrence of agglutination are evaluated. Thus the developed automated method determines the blood type using image processing techniques.The devoloped method is useful in emergency situation to determine the blood group without human errors.
33
star
2

Face-Mask-Dectection

The new Coronavirus disease (COVID-19) has seriously affected the world. By the end of November 2020, the global number of new coronavirus cases had already exceeded 60 million and the number of deaths 1,410,378 according to information from the World Health Organization (WHO). To limit the spread of the disease, mandatory face-mask rules are now becoming common in public settings around the world. Additionally, many public service providers require customers to wear face-masks in accordance with predefined rules (e.g., covering both mouth and nose) when using public services. These developments inspired research into automatic (computer-vision-based) techniques for face-mask detection that can help monitor public behavior and contribute towards constraining the COVID-19 pandemic. Although existing research in this area resulted in efficient techniques for face-mask detection, these usually operate under the assumption that modern face detectors provide perfect detection performance (even for masked faces) and that the main goal of the techniques is to detect the presence of face-masks only. In this study, we revisit these common assumptions and explore the following research questions: (i) How well do existing face detectors perform with masked-face images? (ii) Is it possible to detect a proper (regulation-compliant) placement of facial masks? and iii) How useful are existing face-mask detection techniques for monitoring applications during the COVID-19 pandemic? To answer these and related questions we conduct a comprehensive experimental evaluation of several recent face detectors for their performance with masked-face images. Furthermore, we investigate the usefulness of multiple off-the-shelf deep-learning models for recognizing correct face-mask placement. Finally, we design a complete pipeline for recognizing whether face-masks are worn correctly or not and compare the performance of the pipeline with standard face-mask detection models from the literature. To facilitate the study, we compile a large dataset of facial images from the publicly available MAFA and Wider Face datasets and annotate it with compliant and non-compliant labels. The annotation dataset, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community.
Python
32
star
3

ATTENDANCE-MONITORING-USING-AMAZON-WEB-SERVICES-A.W.S-

An Attendance Monitoring System is designed to manage a user's day to day attendance. Also, the proposed system provides an ease of access since there is no extra paper work. This results in achieving the usage of AWS cloud computing and RFID Interface in an innovative manner, helps us to overcome the traditional attendance.
29
star