In previous posts of this series, I covered some diagnostics improvements I’ve made to help work on more advanced gaits for the mjbots quad A1 (1, 2, 3, 4, 5, 6).  This post will cover the last major new piece of diagnostics I added to tplot2, 3d rendering of telemetry data.

3D rendering

While it should be obvious, I’ll give a little exposition.  tplot2 in its state prior to this could show a “tree view” of all data logged in numeric form.  It had a “plot view” which let you plot any single floating point scalar vs time.  As of recently, it could also render video associated with a given point in time in the log.  However, as anyone who has ever tried to debug a 3d dimensional software application, much less a 3d dimensional robot, can attest, debugging with scalar numbers and time plots is only productive for a very limited range of problems.

This is part of a continuing series on diagnostics tooling for the mjbots quad series of robots.  The previous editions can be found at 1, 2, 3, 4, and 5.  Here, I’ll cover the first extension I developed for tplot2 to make it more useful to diagnose dynamic locomotion issues.

Background

Diagnosing problems on robots is hard.  The data rates are high, sensing is imperfect, and there are many state variables to keep track of.  Keeping track of problems that are related to erroneous perception are doubly challenging.  Without a recording of the ground truth of an event, it can be hard to even know if the sensing was off, or if some other aspect was broken.  Fortunately, for things the size and scope of small dynamic quadrupeds, video recording provides a great way to keep a record of the ground truth state of the machine.  Relatively inexpensive equipment can record high resolution images at hundreds of frames a second documenting exactly where all the extremities of the robot were and what it was doing in time.

In previous posts, (1, 2, 3, 4), I covered the updates I made to the underlying serialization and log file format used in mjlib and the quad A1.  This time I’ll talk about the graphical application that uses that data to investigate live operation.

History

You might note the “2” in the name and realize that yes, this is the second incarnation in the mjmech repository, tplot being the initial.  The original tplot.py was a largely a one-day hack job that glued together the python log bindings I had with matplotlib.  It provided a time scrubber, a tree view, and a plot window where any number of things could be plotted against one another.

While not its primary purpose, I still plan on entering my walking robots in Mech Warfare events when I can.  In that competition, pilots operate the robots remotely, using FPV video feeds.  I eventually aim to get my inertially stabilized turret working again, and when it is working I would like to be able to overlay the telemetry and targeting information on top of the video.

In our previous incarnation of Super Mega Microbot, we had a simple UI which accomplished that purpose, although it had some limitations.  Being based on gstreamer, it was difficult to integrate with other software.  Rendering things in a performant manner on top was certainly possible, although it was challenging enough that in the end we did nothing but render text as that didn’t require quite the extremes of hoop jumping.  Unfortunately, that meant things like the targeting reticule and other features were just ASCII art carefully positioned on the screen.