Implementing Python with ROS: Bridging the Gap Between Code and Robot

Python's simplicity and extensive libraries make it an ideal language for robotics development, and its seamless integration with the Robot Operating System (ROS) further amplifies its power. This chapter delves into the practical aspects of implementing Python with ROS, exploring how to create ROS nodes, publish and subscribe to topics, and leverage Python's capabilities within the ROS framework.  

1. Setting Up Your ROS Python Environment:

• ROS Installation: Ensure ROS is installed on your system.
• Python Dependencies: Install necessary Python libraries, such as rospy, geometry_msgs, and others as needed, using pip.
• Catkin Workspace: Create a catkin workspace to organize your ROS packages.  

2. Creating ROS Python Nodes:

• Node Structure: ROS Python nodes are typically Python scripts that utilize the rospy library.
• Initialization: Every ROS Python node must be initialized using rospy.init_node('node_name').
• Publishers and Subscribers: Nodes can publish messages to topics and subscribe to topics to receive messages.  

3. Publishing and Subscribing to Topics:

• Publishers:
  • Create a publisher using rospy.Publisher('topic_name', message_type, queue_size=10).
  • Publish messages using publisher.publish(message).
• Subscribers:
  • Create a subscriber using rospy.Subscriber('topic_name', message_type, callback_function).
  • The callback_function is executed whenever a new message is received.

4. Working with ROS Messages:

• Message Types: ROS defines various standard message types, such as geometry_msgs/Twist (for velocity commands) and sensor_msgs/LaserScan (for lidar data).
• Creating Messages: Create message objects and populate their fields with data.
• Accessing Message Data: Access message data using the object's attributes.

5. ROS Services:

• Service Servers:
  • Create a service server using rospy.Service('service_name', service_type, service_callback).
  • The service_callback function handles service requests and returns responses.
• Service Clients:
  • Create a service client using rospy.ServiceProxy('service_name', service_type).
  • Call the service using service_client(request).

6. ROS Parameters:

• Setting Parameters: Use rospy.set_param('parameter_name', value) to set parameters.
• Getting Parameters: Use rospy.get_param('parameter_name', default_value) to get parameters.

7. Example: Simple Velocity Controller:

#!/usr/bin/env python
import rospy
from geometry_msgs.msg import Twist

def talker():
    pub = rospy.Publisher('cmd_vel', Twist, queue_size=10)
    rospy.init_node('velocity_controller', anonymous=True)
    rate = rospy.Rate(10) # 10hz
    while not rospy.is_shutdown():
        twist = Twist()
        twist.linear.x = 0.5 # Move forward at 0.5 m/s
        twist.angular.z = 0.1 # Rotate at 0.1 rad/s
        rospy.loginfo(twist)
        pub.publish(twist)
        rate.sleep()

if __name__ == '__main__':
    try:
        talker()
    except rospy.ROSInterruptException:
        pass

This example creates a ROS node that publishes velocity commands to the cmd_vel topic, controlling the robot's movement.

8. Integrating Python Libraries:

• OpenCV: Use OpenCV for computer vision tasks, such as object detection and image processing.  
• NumPy and SciPy: Use NumPy and SciPy for numerical computations, such as sensor data processing and robot kinematics.  
• PySerial: Use PySerial for communicating with serial devices, such as microcontrollers.  

9. Best Practices:

• Modular Design: Break down your robot software into modular nodes that perform specific tasks.  
• Error Handling: Implement robust error handling to prevent your nodes from crashing.
• Logging: Use rospy.loginfo(), rospy.logwarn(), and rospy.logerr() for logging messages.
• Parameterization: Use ROS parameters to make your nodes configurable.  
• Documentation: Document your code and ROS packages.

By combining Python's ease of use with ROS's powerful framework, you can create sophisticated and efficient robot applications.