Getting started in robotics can feel overwhelming. You’ll hear advice ranging from “learn coding first” to “just build a robot.” Both are valid but without structure, you may end up stuck. In this guide, we’ll break down how to start robotics for beginners, show you where ROS2 fits in, and provide a step-by-step path that balances hardware with software.
Along the way, we’ll explore how to learn ROS2 from scratch, what a simple beginner robot looks like, how ESP32 can help, and why SLAM in ROS2 is an important milestone.
Robotics Is Both Hardware and Software: How to Start Robotics for Beginners
Unlike pure software fields like machine learning, robotics demands a grasp of two worlds:
- Hardware → motors, wheels, batteries, microcontrollers, sensors
- Software → algorithms, control systems, communication frameworks like ROS2
This dual requirement makes robotics harder but also more rewarding.
If you only focus on software simulations, your learning curve flattens quickly. On the other hand, starting with hardware gives you tangible results, like a robot moving around your room an essential step in how to start robotics for beginners.
Step 1: Start with a Simple Robot
Before diving deep into ROS2, begin with a physical robot. Even a basic kit teaches you valuable lessons about wiring, power, and motion.
The most common beginner design is the differential drive robot a two-wheel robot that moves forward, backward, or turns by varying wheel speeds.
Other options include:
| Robot Type | Drive System | Common Use | Sensors Typically Used |
| Differential Drive | Two powered wheels | Indoor learning robots | Encoders, IMU, LiDAR |
| Ackermann Drive | Car-like steering | Outdoor robots, cars | GPS, cameras, ultrasonic, radar |
Choosing between indoor vs. outdoor robots depends on your goal. Indoor robots rely more on encoders and IMUs, while outdoor robots need GPS for accurate localization.
Step 2: Add Essential Sensors
To make your robot intelligent, it needs sensors. At a minimum, consider:
- Encoders → wheel rotation and odometry
- IMU (Inertial Measurement Unit) → orientation, acceleration
- GPS → outdoor location tracking
- LiDAR or Depth Camera → environment mapping
Localization is the foundation of autonomy. Every advanced algorithm from autonomous navigation to SLAM ROS2 tutorials depends on knowing your robot’s position.
Step 3: Connect to a Microcontroller
Once you’ve set up motors and sensors, you need a microcontroller to process and send data. For beginners, the ESP32 is a great choice:
- Wi-Fi enabled (wireless robot control)
- Affordable and widely supported
- Compatible with micro-ROS for direct integration with ROS2
Here’s a simple ESP32 sketch for controlling motors:
// ESP32 PWM motor control
const int motor1 = 18;
const int motor2 = 19;
void setup() {
pinMode(motor1, OUTPUT);
pinMode(motor2, OUTPUT);
}
void loop() {
// Move forward
digitalWrite(motor1, HIGH);
digitalWrite(motor2, HIGH);
delay(2000);
// Stop
digitalWrite(motor1, LOW);
digitalWrite(motor2, LOW);
delay(1000);
}This minimal program spins two wheels forward for two seconds, then stops. Later, you can expand it to read sensor data and publish it to ROS2 topics.
Step 4: Learn ROS2 from Scratch
At this stage, you should integrate your robot with ROS2. ROS2 is a middleware it handles communication between software and hardware through nodes, topics, and services.
Instead of visualizing raw data on the microcontroller, you can send it to Rviz2, ROS2’s visualization tool, and plot real-time sensor streams.
Start with these basics:
- ROS2 Topics → publish and subscribe to sensor/motor data
- Rviz2 → visualize LiDAR scans, IMU readings, robot model
- Launch files → automate multi-node startup
Example: subscribing to IMU data in Python
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Imu
class ImuListener(Node):
def __init__(self):
super().__init__('imu_listener')
self.subscription = self.create_subscription(
Imu, 'imu/data', self.listener_callback, 10)
def listener_callback(self, msg):
self.get_logger().info(f'Orientation: {msg.orientation}')
rclpy.init()
node = ImuListener()
rclpy.spin(node)
node.destroy_node()
rclpy.shutdown()This code listens to /imu/data and prints the orientation values.
Step 5: Explore SLAM in ROS2
Once your robot streams data into ROS2, you can attempt SLAM (Simultaneous Localization and Mapping).
SLAM lets a robot map its environment while keeping track of its location in real-time.
Popular ROS2 SLAM packages:
- SLAM Toolbox
- Cartographer
Using SLAM, you’ll create your first 2D occupancy grid map a huge milestone in robotics learning.
Step 6: Don’t Skip Control Systems
Before optimizing hardware, work on the math. Learn:
- Kinematics equations (forward & inverse)
- PID controllers for motor speed
- State estimation algorithms (Kalman Filter, EKF)
- Probability in robotics
A good resource is the open-source book Probabilistic Robotics (MIT Press), which introduces uncertainty handling in real robots.
FAQs
1. Can I start robotics without hardware?
Yes, with simulations (Gazebo, Webots), but hardware accelerates learning and exposes real-world challenges.
2. What is the role of ESP32 in robotics?
ESP32 serves as a low-cost microcontroller for Wi-Fi communication, motor control, and sensor interfacing. It also supports micro-ROS for ROS2 integration.
3. How hard is it to learn ROS2 from scratch?
The basics (nodes, topics, services) can be learned in weeks, but mastery requires practice with real robots and projects.
4. Why is SLAM important?
SLAM enables autonomous navigation by building maps and localizing the robot within them crucial for real-world robotics applications.
5. Which sensors should a beginner prioritize?
Start with encoders and IMU. If budget allows, add LiDAR or a depth camera. GPS is optional unless you plan outdoor robotics.
Wrapping Up
To summarize the how to start robotics for beginners journey:
- Build or buy a simple robot.
- Add essential sensors.
- Use ESP32 for control and wireless data.
- Learn ROS2 from scratch, connecting hardware to software.
- Experiment with SLAM in ROS2.
- Strengthen your foundation with control systems and robotics math.
Following this cycle will accelerate your growth far more than tutorials alone. By the time you complete one full loop from hardware to ROS2 visualization you’ll not just “know ROS2,” you’ll understand robotics.
If you want a structured way to progress, we also offer dedicated robotics learning paths at Robotisim to guide you step by step.