Turtlebot3 Puppy Mode

Overview

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This project implements a comprehensive autonomous mobile robot system built on the Turtlebot3 platform, integrating multiple sensors for navigation, object detection, and human-robot interaction.

System Architecture

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Hardware Configuration

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Sensor Setup

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LiDAR Configuration:

• 360-degree environmental scanning
• 30-degree depth field gradation
• Primary sensor for SLAM

Camera Positioning:

• Positioned 50 pixels above LiDAR
• 15-degree angular coverage per side
• Optimized for sensor fusion with LiDAR data

Key Capabilities

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1. Perception & Detection

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YOLO Object Detection:

# Real-time object detection
def detect_objects(frame):
    results = yolo_model(frame)
    return results.xyxy[0]  # Bounding boxes

Person Detection:

• Specialized algorithms for human tracking
• Synchronized callbacks for temporal data alignment

2. SLAM Navigation

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Simultaneous Localization and Mapping:

# Launch SLAM node
roslaunch turtlebot3_slam turtlebot3_slam.launch

# Launch navigation
roslaunch turtlebot3_navigation turtlebot3_navigation.launch

Features:

• Real-time map building
• Obstacle avoidance
• Path planning
• Autonomous navigation

3. Distance Calculation

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def calculate_distance(lidar_data, angle):
    # Get range at specific angle
    index = int(angle * len(lidar_data) / 360)
    distance = lidar_data[index]
    return distance

4. Sensor Fusion

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Combining LiDAR and camera data:

def sensor_fusion_callback(lidar_msg, camera_msg):
    # Synchronize timestamps
    # Fuse spatial data from LiDAR with visual data from camera
    # Generate unified perception output
    pass

Visualization

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RVIZ Monitoring:

• 3D visualization of robot state
• Real-time sensor data display
• Environmental map rendering
• Path visualization

# Launch RVIZ
roslaunch turtlebot3_bringup turtlebot3_remote.launch
rviz

ROS Node Structure

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/turtlebot3/
├── /scan (LiDAR data)
├── /camera/image_raw (Camera feed)
├── /cmd_vel (Velocity commands)
├── /odom (Odometry)
├── /map (SLAM map)
└── /detection (YOLO results)

Applications

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• Autonomous navigation in indoor environments
• Human following (“Puppy” mode)
• Object detection and tracking
• Security patrol
• Research platform