* builder: Build against Buster * builder: Use correct repository specifications * builder: Move ld.so.preload to have less errors * builder: Use coex repo to install Monkey * builder: Search for buster ROS packages * aruco_pose: Vendor in aruco library from OpenCV 3.4.6 * builder: Move to ROS Melodic * builder: Update kernel version * aruco_pose, clever: Remove opencv3 ROS dependency * builder: Update rosdep * travis: Disable eclint for vendored aruco library * tests: Don't try to locate opencv in ros * roscore: Use melodic distribution * Revert "aruco_pose: Vendor in aruco library from OpenCV 3.4.6" This reverts commit 9c14a8c002bb3396f9a7d9b2ba39969207f066ba. * aruco_pose: Vendor opencv_contrib/aruco again * builder: Add led packages * builder: Remove unused builder code * travis: Add native tests * builder: Set permissions for standalone-install * builder: Use -y for package installation * builder: Add repo for standalone build * builder: Use correct file types for standalone install * aruco_pose: Accept rgb8 map images * builder: Disable mjpg_streamer test * aruco_pose: Allow rgb8 map images (again) * builder: Re-add mjpgstreamer * builder: Install tornado==4.2.1 for rosbridge_suite * builder: Use more recent base image * builder: Use default kernel * builder: Move ld.so.preload back after tests * builder: Disable catkin tests These tests fail on a remote machine but seem to pass just fine on real hardware. Something must have changed between Kinetic and Melodic, and we must investigate more, but for now we just need a working image. * aruco_pose: Remove unused vendored code * selfcheck: Update systemd-analyze regex * builder: Add opencv repository * rosdep: Update package definitions for Melodic * rosdep: Use proper yaml formatting * travis: Remove unnecessary space * docs: Reference Melodic wherever possible
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ROS
Main article: http://wiki.ros.org
ROS is a widely used framework for developing complex and distributed robotic systems.
Installation
Main article: http://wiki.ros.org/melodic/Installation/Ubuntu
ROS is already installed on the RPi image.
To use ROS on a PC, we recommend using Ubuntu Linux (or a virtual machine such as Parallels Desktop Lite](https://itunes.apple.com/ru/app/parallels-desktop-lite/id1085114709?mt=12) or VirtualBox).
Note
For ROS Melodic distribution, we recommend using Ubuntu 18.04.
Concepts
Nodes
Main article: http://wiki.ros.org/Nodes
ROS node is a special program (usually written in Python or C++) that communicates with other nodes via ROS topics and ROS services. Dividing complex robotic systems into isolated nodes provides certain advantages: reduced coupling of the code, increases re-usability and reliability.
Many robotic libraries and the drivers are executed in the form of ROS-nodes.
In order to turn an ordinary program into a ROS node, include a rospy or roscpp library, and insert the initialization code.
An example of a ROS node in Python:
import rospy
rospy.init_node('my_ros_node') # the name of the ROS node
rospy.spin() # entering an endless cycle...
Topics
Main article: http://wiki.ros.org/Topics
A topic is a named data bus used by the nodes for exchanging messages. Any node can post a message in a random topic, and subscribe to an arbitrary topic.
An example of std_msgs/String (line) message type posting in topic /foo in Python:
from std_msgs.msg import String
# ...
foo_pub = rospy.Publisher('/foo', String, queue_size=1) # creating a Publisher
# ...
foo_pub.publish(data='Hello, world!') # posting the message
An example of subscription to topic /foo:
def foo_callback(msg):
print msg.data
# Subscribing. When a message is received in topic /foo, function foo_callback will be invoked.
rospy.Subscriber('/foo', String, foo_callback)
There is also an opportunity to work with the topics using the rostopic utility. For example, using the following command, one can view messages published in topic /variety of the Aegean sea/state:
rostopic echo /mavros/state
Services
Main article: http://wiki.ros.org/Services
A service can be assimilated to the a function that can be called from one node, and processed in another one. The service has a name that is similar to the name of the topic, and 2 message types: request type and response type.
An example ROS service invoking from Python:
from clever.srv import GetTelemetry
# ...
# Creating a wrapper for the get_telemetry service of the clever package with the GetTelemetry type:
get_telemetry = rospy.ServiceProxy('get_telemetry', srv.GetTelemetry)
# Invoking the service, and receiving the quadcopter telemetry:
telemetry = get_telemetry()
You can also work with the services using the rosservice utility. For instance, you can call service /get_telemetry from the command line:
rosservice call /get_telemetry "{frame_id: ''}"
More examples of using the services for Clever quadcopter autonomous flights are available in the documentation for node simple_offboard.
Working on several PCs
Main article: http://wiki.ros.org/ROS/Tutorials/MultipleMachines.
The advantage of using ROS is the possibility of distributing the nodes across several PCs in the network. For example, a node that recognizes an image may be run on a more powerful PC; the node that controls the copter may be run directly on a Raspberry Pi connected to the flight controller, etc.