Artificial Intelligence: Robotics

This tutorial provides a beginner-friendly introduction to Robotics, a subfield of artificial intelligence (AI) that focuses on designing, programming, and deploying intelligent machines capable of interacting with the physical world. As of May 16, 2025, robotics is advancing rapidly, integrating AI techniques like machine learning (ML), computer vision, and natural language processing (NLP) to enable applications in manufacturing, healthcare, and autonomous systems. This tutorial covers the basics of robotics, key concepts, techniques, tools, a hands-on example, and resources for further learning, tailored to the context of AI research areas. No prior robotics experience is assumed, but basic Python knowledge is helpful for the coding section.

What is Robotics?

Robotics involves creating systems that combine hardware (sensors, actuators) and software (AI algorithms) to perform tasks autonomously or semi-autonomously in physical environments. AI-driven robotics leverages ML, computer vision, and decision-making to enable robots to navigate, manipulate objects, and interact with humans.

Examples of Robotics Tasks:

• Navigation: Robots moving through environments (e.g., self-driving cars, warehouse robots).
• Manipulation: Grasping and handling objects (e.g., robotic arms in factories).
• Human-Robot Interaction: Collaborative robots (cobots) assisting humans.
• Swarm Robotics: Coordinating multiple robots for collective tasks.

Key Concepts in Robotics

1. Sensors:
   • Collect data about the environment (e.g., cameras, LIDAR, ultrasonic sensors, IMUs).
   • Example: A camera for visual input, LIDAR for distance mapping.
2. Actuators:
   • Enable movement or action (e.g., motors, servos, grippers).
   • Example: A motor rotates a robot’s wheels.
3. Perception:
   • Processing sensor data to understand the environment.
   • Techniques: Computer vision (e.g., object detection), sensor fusion.
4. Planning & Control:
   • Deciding actions based on goals and sensor data.
   • Techniques: Path planning (e.g., A* algorithm), PID control for motor adjustments.
5. Learning:
   • Using AI to improve performance over time.
   • Techniques: Reinforcement learning (RL), imitation learning.
6. Kinematics:
   • Modeling robot motion (e.g., forward/inverse kinematics for robotic arms).
7. SLAM (Simultaneous Localization and Mapping):
   • Building a map of an unknown environment while tracking the robot’s position.

Tools & Frameworks for Robotics

These tools, widely used in 2025, simplify robotics development:

1. ROS (Robot Operating System):
   • Best for: Modular robot software development.
   • Strengths: Open-source, supports navigation, perception, and control.
2. Gazebo:
   • Best for: Robot simulation.
   • Strengths: Integrates with ROS, simulates physics and sensors.
3. PyTorch & TensorFlow:
   • Best for: Training ML models for perception or decision-making.
   • Strengths: GPU support, flexible for vision and RL.
4. OpenCV:
   • Best for: Computer vision tasks (e.g., object detection).
   • Strengths: Lightweight, real-time processing.
5. Unity/ML-Agents:
   • Best for: Simulating RL for robotics.
   • Strengths: Game-like environments for training.
6. NVIDIA Isaac Sim:
   • Best for: High-fidelity robot simulation.
   • Strengths: GPU-accelerated, supports complex scenarios.

Hands-On Tutorial: Robot Navigation Simulation with ROS and Python

Let’s create a simple robot navigation simulation using ROS 2 (Humble Hawksbill, a stable version in 2025) and Python. The robot will move in a simulated environment (TurtleBot3 in Gazebo) and avoid obstacles using basic sensor data. This example runs on Ubuntu (recommended for ROS) and is beginner-friendly.

Step 1: Set Up Environment

1. Install Ubuntu 22.04: Use a virtual machine or dual-boot if not on Linux.
2. Install ROS 2 Humble:
   • Follow the official guide: https://docs.ros.org/en/humble/Installation.html
   • Basic commands:

   • sudo apt update
     sudo apt install ros-humble-desktop
     source /opt/ros/humble/setup.bash​

3. Install Gazebo and TurtleBot3:

   sudo apt install ros-humble-gazebo-ros-pkgs
   sudo apt install ros-humble-turtlebot3 ros-humble-turtlebot3-simulations

4. Install Python dependencies:
    

   pip install numpy

Step 2: Set Up Workspace

Create a ROS 2 workspace:

mkdir -p ~/ros2_ws/src
cd ~/ros2_ws/src
colcon build
source install/setup.bash​

Step 3: Write the Code

Create a Python node to control the TurtleBot3, making it move forward and avoid obstacles using LIDAR data.

1. Create a package:

   cd ~/ros2_ws/src
   ros2 pkg create --build-type ament_python turtlebot_controller --dependencies rclpy geometry_msgs sensor_msgs

    

    
2. Write the navigation script: Create turtlebot_controller/turtlebot_controller/navigate.py:
   python

   import rclpy
   from rclpy.node import Node
   from geometry_msgs.msg import Twist
   from sensor_msgs.msg import LaserScan
   import numpy as np
   
   class TurtleBotController(Node):
       def __init__(self):
           super().__init__('turtlebot_controller')
           self.publisher = self.create_publisher(Twist, '/cmd_vel', 10)
           self.subscriber = self.create_subscription(
               LaserScan, '/scan', self.laser_callback, 10)
           self.cmd = Twist()
           self.min_distance = 0.5  # Stop if obstacle closer than 0.5m
   
       def laser_callback(self, msg):
           # Get LIDAR data (front 30 degrees)
           ranges = np.array(msg.ranges)
           front = ranges[0:15].tolist() + ranges[-15:].tolist()
           min_dist = min([x for x in front if x > 0.0], default=float('inf'))
   
           # Simple control logic
           if min_dist > self.min_distance:
               self.cmd.linear.x = 0.2  # Move forward
               self.cmd.angular.z = 0.0
           else:
               self.cmd.linear.x = 0.0  # Stop
               self.cmd.angular.z = 0.5  # Turn to avoid obstacle
   
           self.publisher.publish(self.cmd)
           self.get_logger().info(f'Min distance: {min_dist:.2f}m')
   
   def main():
       rclpy.init()
       node = TurtleBotController()
       rclpy.spin(node)
       node.destroy_node()
       rclpy.shutdown()
   
   if __name__ == '__main__':
       main()

    

    
3. Update package files:
   • Edit turtlebot_controller/setup.py to include the script:

     //python
     
     from setuptools import setup
     package_name = 'turtlebot_controller'
     setup(
         name=package_name,
         version='0.0.0',
         packages=[package_name],
         data_files=[
             ('share/ament_index/resource_index/packages', ['resource/' + package_name]),
             ('share/' + package_name, ['package.xml']),
         ],
         install_requires=['setuptools'],
         zip_safe=True,
         maintainer='your_name',
         maintainer_email='your_email@example.com',
         description='TurtleBot Controller',
         license='Apache License 2.0',
         tests_require=['pytest'],
         entry_points={
             'console_scripts': [
                 'navigate = turtlebot_controller.navigate:main',
             ],
         },
     )

      

      
   • Ensure turtlebot_controller/package.xml has dependencies:

   • <depend>rclpy</depend>
     <depend>geometry_msgs</depend>
     <depend>sensor_msgs</depend>

Step 4: Build and Run

1. Build the workspace:

   cd ~/ros2_ws
   colcon build
   source install/setup.bash

    

    
2. Launch the simulation:
   • In terminal 1:

     export TURTLEBOT3_MODEL=waffle
     ros2 launch turtlebot3_gazebo turtlebot3_world.launch.py

      

      
   • This opens Gazebo with TurtleBot3 in a simple environment.
3. Run the controller:
   • In terminal 2:

     source ~/ros2_ws/install/setup.bash
     ros2 run turtlebot_controller navigate

      

      
4. Observe:
   • The TurtleBot moves forward, stops near obstacles, and turns to avoid them.
   • The terminal logs the minimum LIDAR distance.

Step 5: Interpret Results

• Behavior: The robot navigates autonomously, avoiding walls or objects based on LIDAR data.
• Output: Logs show the closest obstacle distance, helping debug the control logic.
• Limitations: This is a basic controller; real-world navigation needs advanced algorithms (e.g., SLAM).

Tips for Improvement:

• Add SLAM: Use ros2 launch turtlebot3_navigation2 navigation2.launch.py for mapping.
• Incorporate Vision: Add OpenCV to process camera data for object detection.
• Use RL: Train the robot with RL (e.g., PPO in PyTorch) for smarter navigation.
• Tweak Parameters: Adjust min_distance or speeds for different environments.