“Line-following robot with AI smarts—Wameedh Scientific Club’s Arduino and Raspberry Pi combo redefines robotics. Join the revolution!
The Line Following Robot project aimed to create an intelligent robot using infrared sensors, empowered with AI capabilities like human detection, car detection, and color recognition through OpenCV. The seamless integration of Arduino and Raspberry Pi technologies facilitated real-time decision-making and control.
Hardware Components
- Chassis
- Motors and Wheels
- Infrared Sensors
- Raspberry Pi
- Arduino Board
- Power Supply
Software Components
- Programming Languages: Python for Raspberry Pi, C++ for Arduino
- OpenCV Integration: Image processing tasks
- AI Algorithms: Pretrained models for human detection, car detection, and color recognition
- Communication Protocols: Serial communication between Arduino and Raspberry Pi
Line Following Algorithm
The robot follows a line based on readings from infrared sensors. For instance, if the middle sensor sends a signal, the robot moves forward; if the right sensor signals, the robot moves to the left, and so on.
AI Tasks Implementation
Human Detection
Data Collection:
- Diverse dataset provided by organizers with various human poses and backgrounds.
Model Training:
- Utilized a pretrained model from Google Colab.
- Applied transfer learning for fine-tuning on the provided dataset.
Integration:
- Seamlessly integrated the pretrained model into the robot’s system.
- Real-time sensor data fed into the model for dynamic responses.
Car Detection
Data Collection:
- Comprehensive dataset from organizers covering diverse car types.
Model Training:
- Adopted transfer learning with a pretrained model from Google Colab.
Integration:
- Integrated the pretrained car detection model with real-time infrared sensor data.
Color Detection
Data Collection:
- Curated dataset covering a spectrum of colors in the robot’s environment.
Model Training:
- Utilized OpenCV for efficient training with specific RGB value ranges.
Integration:
- Streamlined integration of the color detection model into the robot’s system.
Challenges and Solutions
Throughout the project, defining specific RGB value ranges for color detection proved strategically advantageous, mitigating challenges related to lighting variations and environmental conditions.
Results and Achievements
At the League of Robotics event, the robot executed color detection tasks efficiently using OpenCV and predefined RGB value ranges. This underscored the robot’s adaptability in dynamic scenarios and highlighted the effectiveness of our streamlined color detection methodology.
Conclusion
The Line Following Robot project demonstrated successful integration of infrared sensors, AI, and OpenCV. Achievements include Arduino-Raspberry Pi fusion, real-time decision-making, and efficient color detection. Lessons learned include model optimization and data utilization. Future improvements aim to enhance adaptability and refine AI models.”