TEDU Autonomous RC Car
Our flagship project combines cutting-edge technologies to create a self-driving RC vehicle capable of navigating complex environments. The car uses deep reinforcement learning for decision making, computer vision for perception, and sensor fusion for robust localization. This project serves as a testbed for our research on efficient singular autonomous systems.
Key Features
Reinforcement Learning
Deep RL algorithms enable our car to learn optimal driving policies through trial and error, continuously improving its performance.
Computer Vision
Advanced object detection and tracking using deep learning models to perceive and understand the environment in real-time.
Sensor Fusion
Integrating data from multiple sensors including cameras, LiDAR, and IMU for robust perception and localization.
Technology Stack
ROS2
Robot Operating System
PyTorch
Deep Learning
OpenCV
Computer Vision
YOLO
Object Detection
Gazebo
Simulation
NVIDIA Jetson
Edge Computing
ZED Camera
Stereo Vision
Python
Programming
Project Goals
Develop efficient RL models for autonomous navigation
Implement real-time object detection and avoidance
Create a robust sensor fusion pipeline
Test and validate in both simulation and real-world environments
Project Team
Advisor: Prof. Dr. Tolga Kurtuluş Çapın