Robohub.org
 

ROS 101: Drive a Husky!

by
21 March 2014



share this:
ROS101_Clearpath

In the previous ROS 101 post, we showed how easy it is to get ROS going inside a virtual machine, publish topics and subscribe to them. If you haven’t had a chance to check the out all the previous ROS 101 tutorials, you may want to do so before we go on. In this post, we’re going to drive a Husky in a virtual environment, and examine how ROS passes topics around.

An updated version of the Learn_ROS disk is available here:

https://s3.amazonaws.com/CPR_PUBLIC/LEARN_ROS/Learn_ROS-disk1.vmdk
https://s3.amazonaws.com/CPR_PUBLIC/LEARN_ROS/Learn_ROS.ovf

Login (username): user
Password: learn

If you just downloaded the updated version above, please skip the next section. If you have already downloaded it, or are starting from a base install of ROS, please follow the next section.

Updating the Virtual Machine

Open a terminal window (Ctrl + Alt + T), and enter the following:

sudo apt-get update
sudo apt-get install ros-hydro-husky-desktop

Running a virtual Husky

Open a terminal window, and enter:

roslaunch husky_gazebo husky_empty_world.launch

Open another terminal window, and enter:

roslaunch husky_viz view_robot.launch

You should be given two windows, both showing a yellow, rugged robot (the Husky!)

Screenshot-from-2014-03-14-07_34_30

 

The first window shown is Gazebo. This is where we get a realistic simulation of our robot, including wheel slippage, skidding, and inertia. We can add objects to this simulation, such as the cube above, or even entire maps of real places.

Screenshot-from-2014-03-14-07_35_36

The second window is RViz. This tool allows us to see sensor data from a robot, and give it commands (We’ll talk about how to do this in a future post). RViz is a more simplified simulation in the interest of speed.

We can now command the robot to go forwards. Open a terminal window, and enter:

rostopic pub /husky/cmd_vel geometry_msgs/Twist -r 100 '[0.5,0,0]' '[0,0,0]'

In the above command, we publish to the /husky/cmd_vel topic, of topic type geometry_msgs/Twist, at a rate of 100Hz. The data we publish tells the simulated Husky to go forwards at 0.5m/s, without any rotation. You should see your Husky move forwards. In the gazebo window, you might notice simulated wheel slip, and skidding.

Using rqt_graph

We can also see the structure of how topics are passed around the system. Leave the publishing window running, and open a terminal window. Type in:

rosrun rqt_graph rqt_graph

This command generates a representation of how the nodes and topics running on the current ROS Master are related. You should get something similar to the following:

Screenshot-from-2014-03-14-08_10_25

 

The highlighted node and arrow show the topic that you are publishing to the simulated Husky. This Husky then goes on to update the gazebo virtual environment, which takes care of movement of the joints (wheels) and the physics of the robot. The rqt_graph command is very handy to use, when you are unsure who is publishing to what in ROS. Once you figure out what topic you are interested in, you can see the content of the topic using rostopic echo.

Using tf

In Ros, tf is a special topic that keeps track of coordinate frames, and how they relate to each other. So, our simulated Husky starts at (0,0,0) in the world coordinate frame. When the Husky moves, it’s own coordinate frame changes. Each wheel has a coordinate frame that tracks how it is rotating, and where it is. Generally, anything on the robot that is not fixed in space, will have a tf describing it. In the rqt_graph section, you can see that the /tf topic is published to and subscribed from by many different nodes.

One intuitive way to see how the tf topic is structured for a robot is to use the view_frames tool provided by ROS. Open a terminal window. Type in:

rosrun tf2_tools view_frames.py

Wait for this to complete, and then type in:

evince frames.pdf

This will bring up the following image.

Screenshot-from-2014-03-18-12_20_59Here we can see that all four wheel are referenced to the base_link, which is referenced from the base_frootprint. (Toe bone connected to the foot bone, the foot bone….). We also see that the odom topic is driving the reference of the whole robot. This means that if you write to the odom topic (IE, when you publish to the /cmd_vel topic) then the whole robot will move.



tags: , , , , , ,


Clearpath Robotics Clearpath Robotics is dedicated to automating the world's dullest, dirtiest and deadliest jobs through mobile robotic solutions.
Clearpath Robotics Clearpath Robotics is dedicated to automating the world's dullest, dirtiest and deadliest jobs through mobile robotic solutions.





Related posts :



#RoboCup2024 – daily digest: 21 July

In the last of our digests, we report on the closing day of competitions in Eindhoven.
21 July 2024, by and

#RoboCup2024 – daily digest: 20 July

In the second of our daily round-ups, we bring you a taste of the action from Eindhoven.
20 July 2024, by and

#RoboCup2024 – daily digest: 19 July

Welcome to the first of our daily round-ups from RoboCup2024 in Eindhoven.
19 July 2024, by and

Robot Talk Episode 90 – Robotically Augmented People

In this special live recording at the Victoria and Albert Museum, Claire chatted to Milia Helena Hasbani, Benjamin Metcalfe, and Dani Clode about robotic prosthetics and human augmentation.
21 June 2024, by

Robot Talk Episode 89 – Simone Schuerle

In the latest episode of the Robot Talk podcast, Claire chatted to Simone Schuerle from ETH Zürich all about microrobots, medicine and science.
14 June 2024, by

Robot Talk Episode 88 – Lord Ara Darzi

In the latest episode of the Robot Talk podcast, Claire chatted to Lord Ara Darzi from Imperial College London all about robotic surgery - past, present and future.
07 June 2024, by





Robohub is supported by:




Would you like to learn how to tell impactful stories about your robot or AI system?


scicomm
training the next generation of science communicators in robotics & AI


©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association