Skip to content SriTu Hobby 0 My Account Home Read Shop Control About How to make an Obstacle avoidance robot (L298N motor driver) How to make an Obstacle avoidance robot (L298N motor driver) Hello guys, welcome back to my SrTtu Hobby blog. Today we are going to learn how to make an obstacle avoidance robot at a low cost. Do you remember we created an obstacle avoidance robot in the last article? For this, we used a motor shield with an L293D IC. Through this motor drive shield, we were able to control four motors. Click this link and read it. But today we will use the L298N motor driver board for this robot car. We can only control two motors through this motor driver board. Click this link for more information. The functionality of the robot car we describe today is similar to the functionality of the previous robot car article. This robot car can avoid obstacles. An ultrasonic sensor is mainly used for this purpose. We can get the distance through this sensor. Also, we can do this by calculating the obstacle distance range. Next, the servo motor is used to rotate the ultrasonic sensor left and right. This robot uses two toy cars and popsicle sticks for the motors, wheels, and chassis. Because I do this design at a low cost. So, all these components are controlled via the Arduino Uno board. For that, you can use any other Arduino board. Ok, let’s see how does work this robot car This starts to move forward when the power is applied for the first time. After, the robot car then stops when the ultrasonic sensor reaches a distance of less than 20 cm. After even, through the servo motor, the ultrasonic sensor turns left and right and calculates the distance to the obstacles on either side. Then, turn to the long-distance and go forward. This process continues. Okay, now let’s talk about how to do this project step by step. Below are the components you need for this. Also, you can buy these components easily using the given links. Ezoic Arduino Uno board x 1. — Our Store / Amazon Motor driver board x 1. — Our Store / Amazon Ultrasonic sensor x 1. — Our Store / Amazon Servo motor x 1. — Our Store / Amazon Toy cars x 2. Popsicle sticks.– Amazon Jumper wires.— Our Store / Amazon Phone battery x 2. Disclosure: These Amazon links are Affiliate links. As an Amazon Associate, I earn from qualifying purchases. Step 1 Let’s identify these components. Step 2 First, remove the two toy cars and get the parts we need. How to make an Obstacle avoidance robot (L298N motor driver) Step 3 After, set up the robot car chassis using popsicle sticks. How to make an Obstacle avoidance robot (L298N motor driver) Step 4 Next, attach the Arduino Uno board to the top of the chassis. Step 5 How to make an Obstacle avoidance robot (L298N motor driver) Step 6 After, attach the servo motor and the ultrasonic sensor as shown in the pictures below. How to make an Obstacle avoidance robot (L298N motor driver) Step 7 Next, connect these components

1. Enhancing Rail Madad Website
Mini Project 2A
TE-IT
Guided By : Prof. Stella J.
Assistant Professor, Department of Information Technology
3/27/2023 1
Nitish Jha Harsh Kalim
Smit Makwana Vijay Kandala
Group Members
1

2. 2
🞂 Problem Statement
🞂 Literature
🞂 Technical Approach
🞂 Work Flow
🞂 Challenges
🞂 Conclusion & Future Work
Summary/Index

3. 3
 Challenges in the Current System:
In today’s [specific system], users face challenges such as long delays in
processing requests, frequent human errors, and an overall lack of efficiency.
These problems slow down operations and lead to user frustration.
There is also a lack of real-time information, meaning users and staff have to
manually follow up on issues, causing further delays.
 Our Solution:
To tackle these challenges, we are developing a system that integrates AI and
automation. This will streamline the process, ensuring faster, more accurate issue
resolution and minimizing the need for human intervention.
 Key Benefits:
• Real-time updates will provide instant notifications to users.
• Issues will be automatically categorized and routed to the correct team,
ensuring no delays.
• By reducing manual handling, we will minimize human errors and increase
overall system efficiency.
Problem Statement

4. 4
Literature/Survey
 AI-Based Complaint Management:
AI improves efficiency by automating complaint categorization and routing,
reducing manual intervention by 40%.
Reference: ResearchGate - AI in Complaint Management
 Predictive Analytics with Machine Learning:
Machine learning reduces response times by predicting resolution durations
using historical data.
Reference: Predictive Analytics Blog
 Natural Language Processing (NLP):
NLP automates the understanding and categorization of user complaints,
making support systems more efficient.
Reference: NLP in Service Systems
 Real-Time Updates (WebSocket & Firebase):
WebSocket and Firebase deliver instant, real-time updates, keeping users
informed about issue statuses.
Reference: WebSocket and Firebase in Real-Time Systems

5. 5
Technical Approach
 Technologies Used: Our system will be built using proven technologies to
ensure reliability and performance. For the frontend, we are using JavaScript
and Bootstrap to create a responsive and intuitive user interface. The
backend will be powered by Python and Django for fast and secure
processing.
 AI and Machine Learning Models: We are leveraging advanced AI
algorithms like Natural Language Processing (NLP) to automatically
understand and categorize user requests. Machine learning models, including
decision trees and neural networks, will help predict how long it will take to
solve an issue based on past data.
 Database: For efficient data storage and handling, we are using MongoDB, a
flexible and scalable database system that can handle large volumes of data
seamlessly.
 Real-Time Communication: We are utilizing WebSocket to ensure that
users receive real-time updates as soon as their issue status changes.
Additionally, Firebase Cloud Messaging (FCM) will provide push
notifications, so users are always informed, even when they’re not actively
checking the system.

6. 6
WORK FLOW

7. 7
Challenges
 Data Quality:
One of the main challenges is ensuring that the data used by our AI
models is accurate and up-to-date. To address this, we will implement
data validation processes and continuous monitoring.
 System Integration:
Integrating the new system with existing infrastructure can be complex,
especially in older environments. Our strategy includes thorough
testing and collaboration with IT teams to ensure seamless integration.

8.  Conclusion & Future Work:
In conclusion, our project addresses key inefficiencies in the
current system by introducing automation and AI-based decision
making. With real-time tracking and improved issue resolution
times, we expect to significantly enhance both user satisfaction
and operational efficiency.
 Future Enhancements:
o Moving forward, we plan to explore deeper AI integrations,
such as predictive maintenance and IoT-based monitoring to
anticipate issues before they arise.
o We also aim to gather user feedback and continuously improve
the system, making it even more responsive and efficient.
8
Conclusion

9. 9
RESEARCH AND REFERENCES
 REFERENCES
• https://railmadad.indianrailways.gov.in/madad/final/home.jsp
• https://www.railway-technology.com/news/indian-railways-digit
ises-complaint-management-system/?cf-view
 RESEARCH
• https://www.qwak.com/post/considering-feasibility-desirability-and-
viability-when-developing-ai-products
• https://medium.com/@jainvidip/understanding-decision-trees-1ba0e
f5f6bb4
• https://www.slingshotapp.io/blog/predictive-analytics

10. 10
Thank You