Face Tracking Robot

📋 Project Overview

The Face Tracking Robot is an autonomous robotic system that uses computer vision to detect and track human faces in real-time. The robot features a 3-wheeled omnidirectional base for smooth movement and a pan-tilt camera mechanism for precise face tracking. Built using ROS2, the system demonstrates integration of perception, control, and actuation in a complete robotic application.

This project showcases practical robotics skills including sensor integration, control systems, path planning, and real-time computer vision processing, making it an excellent example of end-to-end robotic system development.

💡 Problem Statement

Building an autonomous face tracking system involves several challenges:

• Real-time Processing: Face detection and tracking must operate at video frame rates
• Robust Detection: Handling varying lighting conditions, angles, and occlusions
• Precise Control: Smooth and accurate pan-tilt movements to keep face centered
• Robot Navigation: Coordinating base movement with camera orientation
• System Integration: Seamless communication between vision, control, and actuation modules
• Latency Management: Minimizing delay between detection and robot response

⚡ Solution Approach

The system integrates multiple components:

• Face Detection: Haar cascades or deep learning-based face detection (MTCNN/YOLO)
• Face Tracking: Kalman filter for predictive tracking and smooth following
• Pan-Tilt Control: PID controllers for precise camera positioning
• Robot Base Control: Differential drive or omnidirectional control for base movement
• ROS2 Architecture: Modular nodes for vision, control, and actuation
• Coordinate Transformation: Converting image coordinates to robot frame

🛠️ Technical Implementation

Hardware Components

• Robot Base: 3-wheeled omnidirectional platform with encoders
• Camera System: USB camera or Raspberry Pi camera module
• Pan-Tilt Mechanism: Two servo motors for camera orientation
• Computing Unit: Raspberry Pi or onboard computer for processing
• Motor Controllers: PWM-based control for smooth movement

Software Architecture

• ROS2 Nodes: Modular architecture with separate nodes for each component
• Vision Node: OpenCV-based face detection and tracking
• Control Node: PID controllers for pan-tilt and base movement
• Actuation Node: Motor control and servo positioning
• Topic Communication: Camera images, face coordinates, and control commands
• TF2: Coordinate frame transformations between camera and robot base

Control Algorithms

• Face Detection: Haar cascades or deep learning models for robust detection
• Kalman Filtering: Predictive tracking to handle temporary occlusions
• PID Control: Proportional-Integral-Derivative controllers for smooth tracking
• Coordinate Mapping: Image pixel coordinates to pan-tilt angles
• Velocity Control: Smooth velocity commands for base movement

📚 Key Learnings

• ROS2 Development: Building modular robotic systems with ROS2
• Computer Vision: Real-time face detection and tracking techniques
• Control Systems: PID control implementation for robotic applications
• Robotic Integration: Combining perception, planning, and actuation
• Coordinate Systems: Understanding and managing multiple reference frames
• Real-time Systems: Optimizing for low-latency performance

🚀 Future Enhancements

• Multi-face tracking and person re-identification
• Deep learning-based face recognition for personalized interaction
• Autonomous navigation to follow a person
• Gesture recognition for human-robot interaction
• Integration with SLAM for mapping and navigation
• Voice commands for hands-free control
• Mobile app for remote monitoring and control

Skills Demonstrated