Robot simulation is an essential tool in every roboticist's toolbox. A well-designed simulator makes it possible to rapidly test algorithms, design robots, and perform regression testing using realistic scenarios. Gazebo offers the ability to accurately and efficiently simulate populations of robots in complex indoor and outdoor environments. At your fingertips is a robust physics engine, high-quality graphics, and convenient programmatic and graphical interfaces. Best of all, Gazebo is free with a vibrant community.
With the release of Gazebo 4 came support for Oculus Rift. Since then, a team of students from the National University of Singapore have implemented an engaging and user-friendly software package for navigating through a simulation environment.
Mohit Shridhar, a 2nd year Computer Engineering student, and his partner David Tat Wai Lee, a 2nd year Industrial and Systems Engineering student, completed an internship at HopeTechnik where they had a chance to work with Gazebo and ROS for the first time. During their internship they worked on project SESTO, an AGV designed to carry medical supplies in hospitals. The target hospital was under construction, which left the team with no way to conduct proper tests. Mohit and David were tasked with simulating the AGV's navigation system in Gazebo along with a model of the hospital.
After creating models in Blender and Solidworks, Mohit and David discovered the Oculus Rift support in Gazebo. With the addition of a PS3 joystick, the team had a first-person perspective into the simulated environment. They quickly got carried away, and added a plethora of features to the Gazebo Navigator which can been seen in the video below.
For those interested in using the Oculus-Rift Gazebo Navigator; the code is available at https://github.com/MohitShridhar/oculus_gazebo_navigator.
Utilizing OGRE, Gazebo provides realistic rendering of environments including high-quality lighting, shadows, and textures.
Generate sensor data, optionally with noise, from laser range finders, 2D/3D cameras, Kinect style sensors, contact sensors, force-torque, and more.
Develop custom plugins for robot, sensor, and environmental control. Plugins provide direct access to Gazebo's API.
Many robots are provided including PR2, Pioneer2 DX, iRobot Create, and TurtleBot. Or build your own using SDF.
Run simulation on remote servers, and interface to Gazebo through socket-based message passing using Google Protobufs.
Use CloudSim to run Gazebo on Amazon, Softlayer, or your own OpenStack instance.
Extensive command line tools facilitate simulation introspection and control.
A simple set of steps to get Gazebo up and running rapidly.
The best way to start using Gazebo is to run through the tutorials. These tutorials cover both basic and simple concepts through a series of exercises.
If you can't find what you are looking for, try our askbot help forum located at answers.gazebosim.org.
Still need help? Send a message to the gazebosim mailing list.
A high-level description of Gazebo and its various components.
Doxygen generated documentation for the Gazebo libraries.
A complete list of all the protobuf messages used by Gazebo
SDFormat is an XML file format that defines environments and models. This specification defines all the XML elements for describing world and models.
Gazebo will release a new major version every 6 months. Starting with Gazebo 4.0, releases will occur on the last week of January and July.
The following roadmap is a best guess at the available features for each version. At the time of release more or fewer features may be available.
|Measurement||Gazebo 1.9||Gazebo 2.2||Gazebo 3.0||Gazebo 4.0|
|Lines of code||186k||197k||214k||217k|
|Lines of comments||57k||63k||68k||69k|
|Test function coverage||45.7%||47.1%||41.3%||40.6%|
|Test branch coverage||32.2%||35.5%||29.2%||27.6%|
|Passing tests *||168||376||524||542|
|Failing tests *||0||0||0||0|
|gcc/clang compiler warnings||0||0||0||0|
*Performed on Ubuntu Quantal with Nvidia GPU
Gazebo 3.0+ supports the ODE, Bullet, Simbody and DART physics engines. By default Gazebo is compiled with support for ODE. In order to use the other engines, first make sure they are installed and then compile Gazebo from source.
|Physics Engine||Gazebo Version||Availability||Notes|
|Bullet||3.0+||Source||Gazebo requires libbullet2.82, available in the OSRF repository and to be included in Ubuntu Utopic.|
|Simbody||3.0+||Source||Simbody packages are hosted in the OSRF repository. Expected to appear in Ubuntu Utopic official repositories.|
|DART||3.0+||Source||DART packages are hosted in dartsim PPA. DART is in the process of moving toward inclusion in Ubuntu.|
We are developing a physics plugin framework to resolve dependency issues. Each physics engine will interface to Gazebo through a plugin, avoiding the need to compile Gazebo with support for each engine.
Gazebo development began in the fall of 2002 at the University of Southern California. The original creators were Dr. Andrew Howard and his student Nate Koenig. The concept of a high-fidelity simulator stemmed from the need to simulate robots in outdoor environments under various conditions. As a complementary simulator to Stage, the name Gazebo was chosen as the closest structure to an outdoor stage. The name has stuck despite the fact that most users of Gazebo simulate indoor environments.
Over the years, Nate continued development of Gazebo while completing his PhD. In 2009, John Hsu, a Senior Research Engineer at Willow, integrated ROS and the PR2 into Gazebo, which has since become one the primary tools used in the ROS community. A few years later in the Spring of 2011, Willow Garage started providing financial support for the development of Gazebo. In 2012, Open Source Robotics Foundation (OSRF) spun out of Willow Garage and became the steward of the Gazebo project. After significant development effort by a team of talented individuals, OSRF used Gazebo to run the Virtual Robotics Challenge, a component in the DARPA Robotics Challenge, in July of 2013.
OSRF continues development of Gazebo with support from a diverse and active community. Stay tuned for more exciting developments related to robot simulation.