FPV RC Car with Autonomous Driving
A cutting-edge radio-controlled car platform combining first-person view driving experience with machine learning-powered autonomous navigation capabilities
Project Overview
The YugTroniX FPV RC Car with Autonomous Driving represents our innovative approach to combining traditional radio-controlled vehicles with modern autonomous navigation technology. This project integrates computer vision, machine learning, and robotics to create an educational platform for experimenting with self-driving vehicle concepts.
What makes our RC car unique is its dual-mode operation: users can either drive the car manually with a first-person perspective through a real-time video feed, or engage the autonomous mode where the vehicle navigates independently using its onboard sensors and AI algorithms. This versatility makes it an excellent tool for learning about autonomous driving systems, computer vision, and control theory.
Key Features
- First-Person View System: Low-latency HD camera with wireless video transmission providing real-time driver perspective
- Autonomous Navigation: Machine learning algorithms that enable the car to navigate environments without human input
- Multi-sensor Integration: Fusion of camera data, ultrasonic sensors, and IMU for comprehensive environmental awareness
- Lane Detection: Computer vision system that identifies and follows lane markings
- Obstacle Avoidance: Real-time detection and avoidance of static and moving obstacles
- Remote Monitoring: Web interface for viewing sensor data, video feed, and control parameters
- Dual Control Modes: Seamless switching between manual FPV control and autonomous operation
- Custom Chassis Design: 3D-printed components with optimized weight distribution and sensor mounting points
Technical Specifications
Hardware Components
Software Stack
Performance Metrics
Development Timeline
Initial Design
Concept development, component selection, and architectural planning
Chassis Development
3D modeling, printing, and assembly of the custom chassis
Electronics Integration
Circuit design, component wiring, and power management implementation
FPV System Setup
Camera mounting, video transmission testing, and latency optimization
Software Development
Computer vision algorithms, control software, and web interface creation
Machine Learning Integration
Data collection, model training, and neural network implementation
Testing & Optimization
Real-world testing, parameter tuning, and performance enhancement
Project Team
This project brought together team members with expertise in robotics, software development, electronics, and machine learning to create our innovative autonomous FPV RC car platform.
B.Sri Harsha Vardhan/h3>
Project Lead
Bhavani Sankar
Pavan
J.Latha Sri
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Gallery
Applications
Our FPV RC Car with Autonomous Driving capabilities serves multiple practical applications:
- Educational Platform: Hands-on learning tool for students studying robotics, computer vision, and artificial intelligence
- Algorithm Testing: Test bed for developing and refining autonomous navigation algorithms
- Remote Inspection: Exploration of areas that may be difficult or dangerous for humans to access
- Competitive Racing: Platform for autonomous racing competitions and challenges
- Computer Vision Research: Real-world testing environment for computer vision algorithms and techniques
Technical Challenges
Throughout development, our team overcame several key technical challenges:
- Low-latency Video Transmission: Optimizing the FPV system to minimize lag between camera capture and display
- Real-time Processing: Achieving sufficient processing speed for obstacle detection and decision-making on resource-constrained hardware
- Environmental Adaptation: Creating algorithms robust enough to handle varying lighting conditions and terrain types
- Power Management: Balancing power consumption between propulsion, sensors, and computational resources
- Sensor Fusion: Effectively combining data from multiple sensors for accurate environmental modeling
Future Development
We're continuing to enhance our autonomous RC car with planned improvements including:
- Implementation of SLAM (Simultaneous Localization and Mapping) for improved navigation
- Integration of more sophisticated machine learning models for advanced decision-making
- Addition of GPS for outdoor navigation capabilities
- Development of multi-vehicle communication for cooperative tasks
- Creation of a more intuitive control interface with augmented reality elements
- Enhanced telemetry and data logging for performance analysis
Want to join our FPV RC Car project team?
Become a YugTroniX Club member and contribute to the future of autonomous vehicle technology!
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