Tagged: TM129

First Attempt At Using IPywidgets in Jupyter Notebooks to Display V-REP Robot Simulator Telemetry

Having got a thing together that lets me use some magic to load a V-REP robot simulator scene, connect to it and control a robot contained inside it, I also started to wonder about we could build instrumentation on the Jupyter notebook client side.

The V-REP simulator itself has graph objects that can record and display logged data within the simulator:

But we can also capture data from the simulator as part of the Python control loop running via a notebook.

(I’m not sure if streaming data from the simulator is possible, or how to go about either setting that up in the simulator connection or rendering it in the notebook?)

So here’s my quick starter for 10 getting a simple data display running in a notebook using IPython widgets.

So here’s a simple text display to give a real time (ish) view of a couple of sensor values:

As the robot runs, the widget values update in real-time-ish .

I couldn’t figure out offhand how to generate a live-updating chart, and couldn’t quickly see how to return data from inside the magic cell as part of the magic function. (In fact, I’m not convinced I understand at all the scoping in there!)

But it seems as if we set a global variable inside the magic cell, we can get data out and plot it when the simulation is stopped:

Example notebook here.

If anyone can show me how to create and update a live chart, that would be fantastic:-)

IPython Magic for Controlling the V-REP Robot Simulator from Jupyter notebooks

Whilst exploring how we might be able to use Jupyter notebooks hooked up to the Coppelia Robotics V-REP robot simulator, it struck me that we needed a fair amount of boilerplate stuff to get the simulator loaded with an appropriate scene file and the a connection made to the simulator from the notebook so we could script the robot actions from the notebooks.

My first approach to trying to simplify presentation was to create some “self-documenting” notebooks that could be used to set-up necessary environmental variables and import default classes and functions:

The %run cell magic loads and runs the referenced notebooks, which can also be inspected (and modified) by students.

(To try to minimise the risk of students introducing breaking changes into the imported notebooks, we could also lock the cells as read-only in the notebooks. Whilst this requires an extension to be installed to implement the read-only behaviour, the intention is that we distribute a customised Jupyter notebook environment to students.)

The loadSceneRelativeToClient() function loads the specified scene into the simulator. Note that this scene should contain a robot model. Once the connection to the simulator is made, a robot object can be instantiated using the connection details. The robot class should contain the definitions required to control the robot model in the loaded in scene.

Setting up the connection to the simulator is a bit of a faff, and when code cell execution is stopped we can get an annoying KeyboardInterrupt report:

We can defend against the KeyboardInterrupt by wrapping the code execution in a try/except block:

try:
    with VRep.connect("127.0.0.1", 19997) as api:
        robot = PioneerP3DXL(api)
        while True:
            #do stuff
            pass
except KeyboardInterrupt:
    pass

But it struck me that it would be much nicer to be able to use some magic along the lines of the following, in which we set up the simulator with a scene, identify the robot we want to control, automatically connect to the simulator and then just run the robot control program:

So here’s a first attempt at some IPython cell magic to do that:

from pyrep import VRep
from __future__ import print_function
from IPython.core.magic import (Magics, magics_class, line_magic,
                                cell_magic, line_cell_magic)
import shlex

# The class MUST call this class decorator at creation time
@magics_class
class Vrep_Sim(Magics):

    @cell_magic
    def vrepsim(self, line, cell):
        "V-REP magic"

        #Use shlex.split to handle quoted strings containing a space character
        loadSceneRelativeToClient(shlex.split(line)[0])

        #Get the robot class from the string
        robotclass=eval(shlex.split(line)[1])

        #Handle default IP address and port settings; grab from globals if set
        ip = self.shell.user_ns['vrep_ip'] if 'vrep_ip' in self.shell.user_ns else '127.0.0.1'
        port = self.shell.user_ns['vrep_port'] if 'vrep_port' in self.shell.user_ns else 19997

        #The try/except block exits form a keyboard interrupt cleanly
        try:
            #Create a connection to the simulator
            with VRep.connect(ip, port) as api:
                #Set the robot variable to an instance of the desired robot class
                robot = robotclass(api)
                #Execute the cell code - define robot commands as calls on: robot
                exec(cell)
        except KeyboardInterrupt:
            pass

    #@line_cell_magic
    @line_magic
    def vrep_robot_methods(self, line):
        "Show methods"
        robotclass = eval(line)
        methods = [method for method in dir(robotclass) if not method.startswith('_')]
        print('Methods available in {}:\n\t{}'.format(robotclass.__name__ , '\n\t'.join(methods)))

#Could install as magic separately
ip = get_ipython()
ip.register_magics(Vrep_Sim)

I’ve also added a bit of line magic to display the methods defined on a robot model class:

The tension now is a pedagogical one: for example, should I be providing students with the robot model, or should they be building up the various control functions (.move_forwards(), .turn_left(), etc.) themselves?

I’m also wondering whether I should push the while True: component into the magic? On balance, I think students need to see it in their code block because getting them to think about control loops rather than one shot execution of command statements is something they often don’t get the first, or even second, time round. But for reducing clutter, it’d make for far cleaner cell block code.

Distributing Virtual Machines That Include a Virtual Desktop To Students – V-REP + Jupyter Notebooks

When we put together the virtual machine for TM351, the data management and analysis course, we built a headless virtual machine that did not contain a graphical desktop, but instead ran a set of services that could be accessed within the machine at a machine level, and via a browser based UI at the user level.

Some applications, however, don’t expose an HTML based graphical user interface over http, instead they require access to a native windowing system.

One way round this is to run a system that can generate an HTML based UI within the VM and then expose that via a browser. For an example, see Accessing GUI Apps Via a Browser from a Container Using Guacamole.

Another approach is to expose an X11 window connection from the VM and connect to that on the host, displaying the windows natively on host as a result. See for example the Viewing Application UIs and Launching Applications from Shortcuts section of BYOA (Bring Your Own Application) – Running Containerised Applications on the Desktop.

The problem with the X11 approach is that is requires gubbins (technical term!) on the host to make it work. (I’d love to see a version of Kitematic extended not only to support docker-compose but also pre-packaged with something that could handle X11 connections…)

So another alternative is to create a virtual machine that does expose a desktop, and run the applications on that.

Here’s how I think the different approaches look:

vm_styles

As an example of the desktop VM idea, I’ve put together a build script for a virtual machine containing a Linux graphic desktop that runs the V-REP robot simulator. You can find it here: ou-robotics-vrep.

The script uses one Vagrant script to build the VM and another to launch it.

Along with the simulator, I packaged a Jupyter notebook server that can be used to create Python notebooks that can connect to the simulator and control the simulated robots running within it. These notebooks could be be viewed view a browser running on the virtual machine desktop, but instead I expose the notebook server so notebooks can be viewed in a browser on host.

The architecture thus looks something like this:

I’d never used Vagrant to build a Linux desktop box before, so here are a few things I learned about and observed along the way:

  • installing ubuntu-desktop naively installs a whole range of applications as well. I wanted a minimal desktop that contained just the simulator application (though I also added in a terminal). For the minimal desktop, apt-get install -y ubuntu-desktop --no-install-recommends;
  • by default, Ubuntu requires a user to login (user: vagrant; password: vagrant). I wanted to have as simple an experience as possible so wanted to log the user in automatically. This could be achieved by adding the following to /etc/lightdm/lightdm.conf:
[SeatDefaults]
autologin-user=vagrant
autologin-user-timeout=0
user-session=ubuntu
greeter-session=unity-greeter
  • a screensaver kept kicking in and kicking back to the login screen. I got round this by creating a desktop settings script (/opt/set-gnome-settings.sh):
#dock location
gsettings set com.canonical.Unity.Launcher launcher-position Bottom

#screensaver disable
gsettings set org.gnome.desktop.screensaver lock-enabled false

and then pointing to that from a desktop_settings.desktop file in the /home/vagrant/.config/autostart/ directory (I set execute permissions set on the script and the .desktop file):

[Desktop Entry]
Name=Apply Gnome Settings
Exec=/opt/set-gnome-settings.sh
Hidden=false
NoDisplay=false
X-GNOME-Autostart-enabled=true
Type=Application
  • because the point of the VM is largely to run the simulator, I thought I should autostart the simulator. This can be done with another .desktop file in the autostart directory:
[Desktop Entry]
Name=V-REP Simulator
Exec=/opt/V-REP_PRO_EDU_V3_4_0_Linux/vrep.sh
Type=Application
X-GNOME-Autostart-enabled=true
  • the Jupyter notebook server is started as a service and reuses the installation I used for the TM351 VM;
  • I thought I should also add a desktop shortcut to run the simulator, though I couldnlt find an icon to link to? Create an executable run_vrep.desktop file and place it on the desktop:
[Desktop Entry]
Name=V-REP Simulator
Comment=Run V-REP Simulator
Exec=/opt/V-REP_PRO_EDU_V3_4_0_Linux/vrep.sh
Icon=
Terminal=false
Type=Application

Her’s how it looks:

If you want to give it a try, comments on the build/install process would be much appreciated: ou-robotics-vrep.

I will also be posting a set of activities based on the RobotLab activities used in TM129 in the possibility that we start using V-REP on TM129. The activity notebooks will be posted in the repo and via the associated uncourse blog if you want to play along.

One issue I have noticed is that if I resize the VM window, V-REP crashes… I also can’t figure out how to open a V-REP scene file from script (issue) or how to connect using a VM hostname alias rather than IP address (issue).