Autonomous Robot Car
Build a self-driving car capable of lane detection, obstacle avoidance, and autonomous navigation using computer vision and SLAM. There are levels of difficulty and complexity you can choose from. For example, you can start with a simple car kit that follows a line, or implement your own navigation with LiDAR. You can start small and build complexity as you get more comfortable with the concepts.
Project Overview
A robot car combines wheeled mobility with vision-based autonomy, making it an ideal platform for learning: - Lane following algorithms - Object detection and tracking - Path planning and control - Sensor fusion (camera + ultrasonic/LiDAR)
What You'll Build
- Assemble a 4-wheel drive robot chassis with motor drivers
- Mount camera and distance sensors
- Implement lane detection with OpenCV
- Add SLAM for mapping and localization
- Deploy autonomous navigation with ROS 2 Nav2
Required Knowledge
- Arduino or Raspberry Pi programming
- Computer vision (OpenCV, lane detection, object tracking)
- ROS 2 navigation stack
- Basic electronics (motor control, sensor wiring)
- Path planning algorithms (A*, Dijkstra)
Popular Kits & Platforms
- Elegoo Smart Robot Car (Arduino-based, beginner-friendly)
- JetBot (NVIDIA Jetson Nano, AI-powered)
- DonkeyCar (RC car platform with autonomous racing)
- F1/10 Autonomous Racing (university-level competition platform)
Key Features to Implement
- Basic mobility: Motor control, encoder feedback
- Obstacle avoidance: Ultrasonic sensors, basic reactive control
- Lane following: Camera-based line detection
- Mapping: SLAM with 2D LiDAR
- Autonomy: Waypoint navigation, traffic sign recognition
Resources
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