Project Goals: Create a mobile robot that can navigate through an environment while avoiding obstacles using sensors. Implement basic machine learning techniques to improve navigation and decision-making. Components: Hardware:

Chassis Kit: A robot chassis with wheels. Microcontroller: Arduino or Raspberry Pi. Motors: DC motors with motor drivers (like L298N) to control movement. Sensors: Ultrasonic Sensor: For distance measurement to detect obstacles (e.g., HC-SR04). Infrared Sensors: To assist in line following or detecting edges. Power Supply: rechargeable battery or a power bank. Software:

Programming Language: Python (for Raspberry Pi) or C/C++ (for Arduino). Machine Learning Libraries: If using Python, libraries like Scikit-learn or TensorFlow for any decision-making models. Control Libraries: Libraries for motor control and sensor readings (e.g., Arduino’s Servo library). Steps to Build the Project: Assemble the robot:

Build the robot chassis by assembling wheels, motors, and attaching the microcontroller. Connect the motors to the motor driver and then to the microcontroller. Integrate Sensors:

Attach the ultrasonic sensor to the front of the robot to measure distance to obstacles. If using infrared sensors, position them on the sides or bottom of the robot for edge detection or line following. Programming the Robot:

Basic Movement Control: Write code to control the motors, allowing the robot to move forward, backward, and turn. Obstacle detection: Implement logic to read distance data from the ultrasonic sensor. If an obstacle is detected within a certain distance, make the robot stop or turn to avoid it. Implement Obstacle Avoidance Logic:

Use the distance readings to determine the best course of action when an obstacle is detected. Implement decision-making algorithms (e.g., simple rule-based logic or more advanced algorithms) to navigate around obstacles: If the front sensor detects an obstacle, turn left or right based on additional sensor readings. If no obstacles are detected, continue moving forward. Machine Learning (Optional):

If you want to enhance the project, collect data on how the robot behaves in different environments. Train a simple model to predict the best movement based on sensor inputs and past decisions (like reinforcement learning for navigation). Testing and Iteration:

Test the robot in various environments to evaluate its obstacle-avoidance capabilities. Make adjustments to the code and logic based on testing results. Enhancements:

Line Following: Integrate additional sensors to allow the robot to follow a line or path. Remote Control: Add wireless control using Bluetooth or Wi-Fi for manual operation. Camera Integration: Integrate a camera for visual feedback or additional obstacle detection using computer vision techniques. Benefits of This Project: Hands-on experience with robotics, including motor control and sensor integration. Introduction to programming and debugging in a real-world scenario. Opportunity to explore basic machine learning concepts if you choose to implement them. Ability to expand the project with additional features, such as remote control or advanced navigation. This project is an excellent way to start learning about robotics and automation while allowing for creativity and the application of fundamental engineering principles.