Autonomous Model Car - Final Study Project

Project Overview

Final study project focused on developing an autonomous model car using neural networks for path planning and obstacle avoidance. The project earned a grade of 1.2 for its innovative approach and successful implementation.

Key Features

• Real-time computer vision processing
• Neural network-based path planning
• Obstacle detection and avoidance
• Raspberry Pi-based control system
• Custom 3D-printed chassis components

Technical Implementation

The system uses a Raspberry Pi with a mounted camera for real-time environment analysis. TensorFlow models handle path planning and obstacle avoidance, while OpenCV processes the video feed for object detection and lane following.

Challenges & Solutions

• Challenge: Real-time processing on limited hardware
  Solution: Optimized neural network for edge deployment
• Challenge: Reliable obstacle detection
  Solution: Implemented multi-stage detection pipeline
• Challenge: Smooth control implementation
  Solution: Developed PID controller with path smoothing

Results & Impact

The project successfully demonstrated autonomous navigation through complex test courses with reliable obstacle avoidance. The implementation serves as a practical example of applying machine learning in robotics applications.