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TurtleBot3

Turtlebot3

TurtleBot3_Video

The TurtleBot3 is a popular educational and research robot platform developed by ROBOTIS in collaboration with Open Robotics. It's widely used for learning robotics, SLAM, navigation, and autonomous systems development.

System Overview

TurtleBot3 is a compact, affordable, and highly customizable mobile robot designed for education, research, and hobby applications. It features a modular design that supports various sensor configurations and computing platforms.

Key Features

  • Compact Design: Small footprint ideal for indoor navigation
  • Modular Architecture: Customizable sensor and hardware configuration
  • ROS Native: Full ROS/ROS2 integration and support
  • Educational Focus: Extensive documentation and learning resources
  • Open Source: Hardware and software designs freely available
  • Multiple Variants: Burger, Waffle, and Waffle Pi configurations

Specifications

  • Dimensions: Approximately 178mm × 178mm × 192mm (Burger model)
  • Weight: ~1kg (varies by model and configuration)
  • Max Speed: ~0.22 m/s linear, ~2.84 rad/s angular
  • Battery Life: ~2.5 hours (varies by usage)
  • Wheel Configuration: 2-wheel differential drive
  • Computing: Raspberry Pi or similar SBC

Components

Drive System

  • Motors: Two servo motors (Dynamixel XL430-W250-T)
  • Wheels: Two main drive wheels with omni-directional caster wheel
  • Encoders: Built-in position feedback in Dynamixel servos
  • Drive Type: Differential drive kinematics

Standard Sensors

  • LiDAR: 360-degree laser range finder (LDS-01 or LDS-02)
  • IMU: 9-axis inertial measurement unit
  • Odometry: Wheel encoder-based position estimation
  • Optional: Camera (RealSense, Raspberry Pi camera)

Computing Platform

  • Single Board Computer: Raspberry Pi 3B+ or 4
  • Microcontroller: OpenCR board for low-level control
  • Communication: Wi-Fi, Ethernet, Serial interfaces

Available Functions

The TurtleBot3 example includes comprehensive MATLAB functions for Simulink integration:

  • wb_robot_step.m - Main simulation step function
  • wb_motor_set_velocity.m - Differential drive velocity control
  • wb_motor_set_position.m - Motor position control
  • wb_motor_get_position_sensor.m - Wheel encoder readings
  • wb_gyro_get_values.m - IMU gyroscope data
  • wb_accelerometer_get_values.m - IMU accelerometer data
  • wb_lidar_get_range_image.m - LiDAR scan data processing
  • wb_lidar_get_horizontal_resolution.m - LiDAR configuration

State-Space Modeling

The included Simulink model (state_space_modeling.slx) provides:

  • Differential Drive Kinematics: Forward and inverse kinematic models
  • Sensor Fusion: IMU and odometry integration
  • Navigation Control: Velocity and trajectory control
  • SLAM Integration: Real-time mapping capabilities
  • Obstacle Avoidance: LiDAR-based collision avoidance

Control System Design

Differential Drive Kinematics

The TurtleBot3 uses differential drive kinematics where linear and angular velocities are controlled through left and right wheel speeds:

% Forward kinematics
v = (v_left + v_right) / 2;      % Linear velocity
w = (v_right - v_left) / L;      % Angular velocity

% Inverse kinematics  
v_left = v - (w * L) / 2;        % Left wheel velocity
v_right = v + (w * L) / 2;       % Right wheel velocity

Where: - v: Linear velocity (m/s) - w: Angular velocity (rad/s) - L: Wheelbase distance (m)

Control Architecture

  1. Low-level Control: Motor velocity control with PID feedback
  2. Motion Control: Velocity and trajectory following
  3. Navigation: Path planning and execution
  4. SLAM: Simultaneous localization and mapping
  5. Behavior: Task-level behavior coordination
  • Localization: AMCL (Adaptive Monte Carlo Localization)
  • Global Planning: A, RRT, or Dijkstra path planning algorithms
  • Local Planning: DWA (Dynamic Window Approach) for obstacle avoidance
  • Costmaps: Static and dynamic obstacle representation
  • Recovery Behaviors: Stuck and obstacle recovery strategies

SLAM Capabilities

  • Gmapping: Grid-based SLAM using laser scan data
  • Cartographer: Google's real-time SLAM solution
  • Hector SLAM: Fast 2D SLAM without odometry requirement
  • RTAB-Map: 3D RGB-D SLAM (with camera)

Usage Examples

Basic Movement Control

# Move forward
linear_velocity = 0.2   # m/s
angular_velocity = 0.0  # rad/s

# Turn in place
linear_velocity = 0.0   # m/s  
angular_velocity = 0.5  # rad/s

# Arc movement
linear_velocity = 0.15  # m/s
angular_velocity = 0.3  # rad/s
  1. Load World: Open turtlebot3/worlds/world.wbt
  2. Configure Controller: Set to simulink_control_app
  3. Open Model: Load state_space_modeling.slx in MATLAB
  4. Set Parameters: Configure PID gains and sensor settings
  5. Run Simulation: Execute with real-time data exchange

Common Applications

  1. Autonomous Navigation: Goal-based navigation with obstacle avoidance
  2. SLAM Mapping: Real-time environment mapping
  3. Follow-Me Robot: Person following using camera or LiDAR
  4. Multi-Robot Systems: Coordination of multiple TurtleBot3 units
  5. Educational Demos: Teaching robotics concepts

Educational Applications

Learning Objectives

The TurtleBot3 platform is excellent for teaching:

  • Mobile Robot Kinematics: Differential drive mathematics
  • Sensor Integration: LiDAR, IMU, and encoder fusion
  • Control Systems: PID control and feedback systems
  • Path Planning: A*, RRT, and potential field methods
  • SLAM Algorithms: Mapping and localization techniques
  • ROS Concepts: Node communication and system architecture

Laboratory Exercises

  1. Basic Movement: Implement teleoperation and waypoint navigation
  2. Sensor Processing: LiDAR data filtering and obstacle detection
  3. Mapping: Create maps using different SLAM algorithms
  4. Navigation: Implement autonomous navigation stack
  5. Multi-Robot: Coordinate multiple robots for formation control

Performance Characteristics

Motion Capabilities

  • Max Linear Speed: 0.22 m/s
  • Max Angular Speed: 2.84 rad/s
  • Minimum Turning Radius: Zero (turn in place)
  • Typical Operating Speed: 0.1-0.15 m/s for navigation

Sensor Performance

  • LiDAR Range: 0.12m - 3.5m
  • LiDAR Accuracy: ±3cm
  • Update Rate: 10-15 Hz (typical navigation frequency)
  • IMU Drift: <1°/hour (typical gyro drift)

References

Educational Purpose: The TurtleBot3 simulation provides a comprehensive platform for learning mobile robotics, SLAM, navigation, and control systems. Its integration with Simulink enables rapid prototyping of control algorithms and provides a bridge between theoretical concepts and practical implementation. The platform is widely used in robotics courses worldwide and serves as an excellent introduction to autonomous mobile robotics.