MicroPython and ROS: Bridging the Gap between Microcontrollers and Robotics

Table of Contents#

1. Fundamental Concepts
   • What is MicroPython?
   • What is ROS?
   • Why Combine MicroPython and ROS?
2. Usage Methods
   • Setting up the Environment
   • Communication between MicroPython and ROS
3. Common Practices
   • Publishing and Subscribing Messages
   • Service Calls
4. Best Practices
   • Code Organization
   • Error Handling
5. Conclusion
6. References

Fundamental Concepts#

What is MicroPython?#

MicroPython is a full Python compiler and runtime that runs on a microcontroller. It allows developers to write Python code to control hardware directly, such as sensors, actuators, and communication interfaces. MicroPython supports a wide range of microcontroller platforms, including the Raspberry Pi Pico, ESP32, and STM32.

What is ROS?#

The Robot Operating System (ROS) is a flexible framework for writing robot software. It provides a collection of libraries and tools for tasks such as hardware abstraction, low - level device control, message passing between processes, and package management. ROS uses a publish - subscribe model for communication between different nodes in a robotic system.

Why Combine MicroPython and ROS?#

• Ease of Programming: Python is a high - level language with a simple syntax, which makes it easy to develop and debug code. MicroPython allows developers to use Python on microcontrollers, reducing the learning curve for embedded systems development.
• Cost - Effectiveness: Microcontrollers are generally less expensive than traditional computers. By using MicroPython on microcontrollers and integrating them with ROS, we can build cost - effective robotic systems.
• Lightweight and Portable: MicroPython has a small footprint, making it suitable for resource - constrained devices. This allows us to create lightweight robotic components that can be easily integrated into larger systems.

Usage Methods#

Setting up the Environment#

1. Install MicroPython on the Microcontroller:
   • For example, if you are using an ESP32, you can download the latest MicroPython firmware from the official website and flash it to the ESP32 using a tool like esptool.py.

   esptool.py --chip esp32 --port /dev/ttyUSB0 --baud 460800 write_flash -z 0x1000 esp32-20220117-v1.18.bin

2. Install ROS on the Host Computer:
   • Follow the official ROS installation guide for your operating system (e.g., Ubuntu). For ROS 2 Humble on Ubuntu 22.04:

   sudo apt update
   sudo apt install ros - humble - desktop

3. Establish Communication:
   • You can use a serial connection or a network connection between the microcontroller and the host computer. For example, if using a serial connection, you can use the pyserial library in Python on the host computer to communicate with the MicroPython device.

Communication between MicroPython and ROS#

To establish communication between MicroPython on the microcontroller and ROS on the host computer, we can use a custom protocol or existing communication libraries. One common approach is to use the rosserial library, which provides a simple way to communicate between a microcontroller and ROS.

Common Practices#

Publishing and Subscribing Messages#

1. MicroPython Side (Publishing):

import machine
import time
import ubinascii
import usocket as socket
 
# Simulate sensor data
sensor_value = 0
 
# Create a UDP socket
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
server_address = ('192.168.1.100', 12345)
 
while True:
    sensor_value += 1
    message = str(sensor_value).encode()
    sock.sendto(message, server_address)
    time.sleep(1)

2. ROS Side (Subscribing):

import rclpy
from rclpy.node import Node
import socket
 
class SensorSubscriber(Node):
    def __init__(self):
        super().__init__('sensor_subscriber')
        self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
        self.sock.bind(('0.0.0.0', 12345))
        self.get_logger().info('Waiting for sensor data...')
        self.timer = self.create_timer(0.1, self.receive_data)
 
    def receive_data(self):
        data, address = self.sock.recvfrom(1024)
        sensor_value = int(data.decode())
        self.get_logger().info(f'Received sensor value: {sensor_value}')
 
def main(args=None):
    rclpy.init(args=args)
    subscriber = SensorSubscriber()
    rclpy.spin(subscriber)
    subscriber.destroy_node()
    rclpy.shutdown()
 
if __name__ == '__main__':
    main()

Service Calls#

1. MicroPython Side (Service Client):

import usocket as socket
 
# Create a TCP socket
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_address = ('192.168.1.100', 54321)
sock.connect(server_address)
 
# Send a service request
request = 'get_status'
sock.sendall(request.encode())
 
# Receive the response
response = sock.recv(1024).decode()
print(f'Received response: {response}')
 
sock.close()

2. ROS Side (Service Server):

import rclpy
from rclpy.node import Node
import socket
 
class ServiceServer(Node):
    def __init__(self):
        super().__init__('service_server')
        self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        server_address = ('0.0.0.0', 54321)
        self.sock.bind(server_address)
        self.sock.listen(1)
        self.get_logger().info('Waiting for service requests...')
        self.timer = self.create_timer(0.1, self.handle_request)
 
    def handle_request(self):
        connection, client_address = self.sock.accept()
        try:
            data = connection.recv(1024).decode()
            if data == 'get_status':
                response = 'OK'
            else:
                response = 'Unknown request'
            connection.sendall(response.encode())
        finally:
            connection.close()
 
def main(args=None):
    rclpy.init(args=args)
    server = ServiceServer()
    rclpy.spin(server)
    server.destroy_node()
    rclpy.shutdown()
 
if __name__ == '__main__':
    main()

Best Practices#

Code Organization#

• Modularize Code: Break your code into smaller functions and classes. For example, in the MicroPython code, you can create separate functions for sensor data acquisition and communication.
• Use Libraries: Leverage existing libraries to simplify development. For ROS, use the official ROS libraries and for MicroPython, use built - in and third - party libraries.

Error Handling#

• MicroPython: Use try - except blocks to handle exceptions. For example, when communicating with the host computer, handle socket errors.

import usocket as socket
 
try:
    sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
    server_address = ('192.168.1.100', 12345)
    message = 'test'.encode()
    sock.sendto(message, server_address)
except OSError as e:
    print(f'Socket error: {e}')

• ROS: In ROS nodes, use appropriate error handling mechanisms provided by the ROS framework. For example, handle exceptions when creating publishers, subscribers, or services.

Conclusion#

Combining MicroPython and ROS offers a powerful and flexible way to develop robotic systems. By understanding the fundamental concepts, using the right usage methods, following common practices, and implementing best practices, developers can create cost - effective, lightweight, and easy - to - program robotic components. This integration bridges the gap between microcontrollers and the rich ecosystem of ROS, opening up new possibilities for the future of robotics.

References#

• MicroPython official website: https://micropython.org/
• ROS official website: https://www.ros.org/
• rosserial documentation: http://wiki.ros.org/rosserial
• ESP32 MicroPython documentation: https://docs.micropython.org/en/latest/esp32/quickref.html