Tag Archives: gazebo

legtool now on github

legtool is the library and graphical application I’ve developed for inverse kinematics and gait generation to use with Super Mega Microbot. I’ve pushed a public version of it to github, in the hopes that it may be useful for other developers of legged robots.

I’ve also put together a short demonstration video, showing how to download, install, and use legtool with a gazebo simulated quadruped.


In lieu of actual documentation (or possibly to start generating the text of some), I’ve written some text describing what legtool does and how to to use it below.

legtool overview and installation

legtool is a python library and PySide based graphical application useful for pose generation, inverse kinematics, and gait generation for legged robots. It consists of a set of python libraries, and a graphical debugging application which uses those libraries to allow a developer to explore legged locomotion. It has primarily been tested on Ubuntu linux, but may be portable to other operating systems.

legtool is not yet packaged for installation, so all you need to do is clone the github repository and install the dependencies as documented in the README.

git clone https://github.com/jpieper/legtool.git

Legtool by default creates a configuration file in ~/.config, however, you can specify an alternate configuration on the command line using the -c option. If you have multiple robots, or a simulation and an actual robot, you will want to keep multiple configurations around.

Servo tab

The first tab which is active, the “servo” tab, contains controls to select the servos to control and configure them, along with controls to maintain a list of named poses, where each pose consists of a set of servo positions.

legtool servo tab


Here you can move servos individually to verify their operation, and compose poses. The only use for poses currently is in the later inverse kinematics tabs, so there is no need to define more than idle, minimum, and maximum.

Inverse kinematics tab

The second tab, the IK tab, lets you configure which servos are in which leg, what sign they have, and also the geometry of the legs. The lower right corner visualizes the achievable region of operation in 2D. You can select alternate projections, and change the centerpoint of the test using the IK Offset group. Clicking on the rendering will command that leg to that position.

legtool IK tab

Gait tab

The third, and currently final tab, contains controls to allow configuring the placement of all shoulder joints, marking the center of gravity, and configuring the gait engine. It provides several debugging facilities to allow visualizing the results of the gait engine.

legtool gait tab

The geometry configuration is in the upper left. The shoulder position can be set individually for each leg. The center of gravity and idle position are common to all legs. The gait configuration area in the lower left selects which gait to use, and then exposes a number of configuration options for that gait.

The remainder of the tab is devoted to interactive tools for debugging the gaits:

  • Gait graph: The gait graph in the upper middle shows for the duration of a single cycle, when each of the legs is in the air.
  • Geometry view: Visible in the upper right, this shows a 2D rendering of where the shoulders, legs, body, center of gravity, and support polygon are located.
  • Graphical command view: Visible in the lower right, the graphical command view shows which commands are feasible given the current geometry and gait configuration. Any two command axes can be selected, after which the feasible region is shown in green, regions where the speed is limited are in yellow, and totally infeasible regions are in red.
  • Playback: The playback scrubber allows you to slide back and forward through a gait cycle, visualizing the results in the geometry view or on the simulated or real robot.
  • Textual command area: All of the 9 degrees of command freedom are settable here, in addition to the two available in the graphical command view.

pygazebo 3.0.0-2014.1

Yesterday I pushed the release of the newest version of pygazebo to github, 3.0.0-2014.1. This version has two big changes, the first is that I’ve updated all the message bindings for gazebo 3.0.0, which gets two new messages, CameraCmd and SphericalCoordinates. The second, and larger change, is a switch from eventlet to trollius for asynchronous coroutine operation.

trollius is an implementation of the python3 asyncio/tulip library for python2. It is largely API compatible, with the biggest exception being that as a library, trollius can’t support the new python3 “yield from” syntax. pygazebo internally uses an API compatible subset.

The biggest reasons to switch were:

  • Explicit control flow: In the asyncio model, the only ways of yielding control are by returning from a function, or “yield”ing from a coroutine. This means that you can reason locally about whether synchronization is appropriate, even when calling other methods.
  • Integration with other event loops: While eventlet was nominally capable of being integrated with other event loops, in practice it wasn’t very easy and no one did it. The best integration strategy was usually to poll at a high frequency in another event loop and pump the eventlet loop. asyncio already has a draft event loop to interoperate with GLib (gbulb), and writing a basic one to interoperate with QT was not challenging.
  • Unit test coverage: Eventlet (and other greenlet based systems) had a long-standing issue where most python unit test systems could not accurately track test coverage. trollius and asyncio seem to work just fine here.

The new release is on github and pypi, so a newer pygazebo is only a “pip install pygazebo” away.

pygazebo – First release

I managed to clean up the python bindings to Gazebo I wrote when testing mech gaits in simulation, and have released them publicly as pygazebo.

All you have to do is:

pip install pygazebo

From the README, a simple example:

import eventlet
from pygazebo import Manager

manager = Manager()
publisher = manager.advertise('/gazebo/default/model/joint_cmd',

message = pygazebo.msg.joint_cmd_pb2.JointCmd()
message.axis = 0
message.force = 1.0

while True:

and you’re off and ready to code!

PyPose + Gazebo for Mech Warfare

Mikhail and I are considering fielding an entry into Mech Warfare this season. To evaluate different geometries and servo models, I tried setting up a simple simulation environment which would let us experiment without having to have a large variety of hardware on hand. While we’re not finished, I have a minimal first proof of concept working now, which I’ll describe briefly.

Immortal, competing in Mech Warfare at the KC Maker Faire


The simulator I’m using is Gazebo. It integrates several different rigid body physics engines, a 3D visualization environment, and a relatively simple file format for describing the configuration of robots. It uses a client server publish-subscribe model, where a central server maintains the physics simulation and any number of clients can connect to control or monitor individual models.

For gait generation, I’m starting with PyPose, which is a pose sequencer and inverse kinematics engine for the arbotiX controller, an open source Arduino compatible controller for Dynamixel servos. Specifically, the NUKE, or Nearly Universal Kinematics Engine, contains routines for generating a couple of different gait patterns for walking robots with lizard style legs.


The nominal workflow I wanted was to operate PyPose on a synthetic robot, simulated by Gazebo. I had to add a couple of pieces of software to make that happen.

To start, Gazebo doesn’t really have a documented protocol for interacting with its publish subscribe network. The primary way clients use it currently is through ROS. To make this work, I wrote up a simple python client library which implements the publish subscribe protocol using eventlet. This allows python applications to both subscribe to topics, as well as publish them.

Next, PyPose is very specialized to the Dynamixel servos and the arbotiX controller. Additionaly, from a gait generation perspective, it is effectively composed of effectively two independent parts. The first is an ahead of time pose sequencer and configuration tool written in python. The second, is a generated Arduino sketch which implements the actual gait when run on an AVR controller. The former, I hacked in a simple abstraction layer and connected it to the python Gazebo library. For the latter, I actually ported the generated C code back into python in order to test the generated gaits in simulation.


At the moment, I have only a rough proof of concept… the code is hacky, I haven’t yet simulated the physical characteristics of any particular servo, the physics model doesn’t seem quite right yet, and the Gazebo model consists of nothing but jointed rectangles with no textures. Despite that, in the video below, you can still see both PyPose manipulating the model, and the python gait generator operating it.


Next up, I’ll be trying to polish off the rough edges, and then try to evaluate the robot configuration variants I actually wanted to validate.