Now that 64 bit Raspberry Pi OS is a real thing, I finally got around to updating the pi3hat libraries. Now, as of pypi 0.3.20, you can use the pi3hat library to run console only or graphical tview on the most recent Raspberry Pi OS!
For most of mjbots’ existence, all of our products were labeled with a dependable, if lackluster, Brother P-Touch label maker. In line with other packaging improvements, I recently upgraded that labeling setup to bigger, higher resolution, and full color!
This is using an Epson TM-C3500, which I had expected to operate directly from my Linux based test fixtures. However, upon receiving it, discovered that alas only Windows drivers were available. Thankfully, it wasn’t too bad to print from python in a simple way as long as you manually select the media type from the Windows dialogs. Thus I made up a simple Flask app to receive label images over HTTP and print them. That runs on a Windows computer, and the test fixture applications just POST their label images to it.
The pi3hat, among other things has 5 CAN-FD ports. You can use them to drive a lot of moteus servos, but they are perfectly fine CAN-FD ports generally. The C++ library has always been able to send and receive arbitrary frames (and recently at arbitrary bitrates), but the python interface was lacking, only exposing a portion of this functionality.
As of version 0.3.11, the python library (pip3 install moteus-pi3hat) now exposes everything you need to be able to send and receive arbitrary CAN frames from any of the ports, as well as configure all the timing options for waiting for responses from slave devices.
To date, the pi3hat CAN channels only supported CAN properties suitable for use with moteus controllers. Given that’s what most people are using them for, that’s fine. However, there was no real constraint behind that, just laziness.
Thus, I’ve released new firmware for the pi3hat that supports configuring the bitrate, FD-ness, and other properties of all 5 CAN channels.
Currently only the C++ library exposes the configuration functionality, but it will be easy enough to add to python when someone needs it.
After receiving many requests via youtube, discord, and email, I’ve finally gone ahead, bitten the bullet, and updated all of the moteus tools to be pure python and work in a cross platform manner. Now, the only thing you need to do to install pre-compiled versions of tview and moteus tool on most* platforms is:
pip3 install moteus_gui
python3 -m moteus_gui.tview # (or maybe just tview)
python3 -m moteus.moteus_tool # (or maybe just moteus_tool)
I’ve personally tested these on Linux, Windows, and Raspberry Pi, and others have at least verified basic operation on Macs. Python 3.7 or greater is required.
NOTE 2025-10-29: An updated version of this post can now be found in the official moteus documentation:
I’m excited to announce new python bindings for communicating with moteus controllers! A simple example from the README:
import asyncio
import math
import moteus
async def main():
c = moteus.Controller()
print(await c.set_position(position=math.nan, query=True))
await asyncio.sleep(1.0)
asyncio.run(main())
This code will try to locate an fdcanusb on your host and use it to communicate with controller with ID 1. All of those details can be customized through code depending upon how you construct things. The library is pure python, although it doesn’t work on Windows currently because it relies on an asyncio aware pyserial wrapper that doesn’t work there.
I’m working to improve the walking gait of the mjbots quad A1. In this iteration, I wanted to tackle an incremental step towards a more fully dynamic gait, but one that will still greatly increase the capability of the machine. As mentioned last time, the current walking gait cycles between all four legs, and then alternative opposing corner legs in order to move laterally. I’d like to keep that same basic structure, but be a bit smarter about what happens during the swing phase.
I’ve had a confusing mismash of development tools for the moteus servos for a while now. My original development tool was in python, which worked just fine. Coroutines allowed me to express complex asynchronous logic succinctly, the program itself was rather simple, and I could easily integrate it with matplotlib for plotting. However, when looking to run this on the raspberry pi, I needed a newer python version than came with raspbian, which turned out to be a royal pain to get installed in a repeatable manner. Thus I rewrote a portion of the moteus_tool in C++ and just used my normal cross-compiling toolchain to generate the binaries. What I didn’t do was port the calibration logic, as the state machine required with standard boost::asio would have bloated the logic size by 5x, and I didn’t really need to calibrate servos from the raspberry pi ever.
While working to build a weight reduced moteus servo mk2, I reworked my outer housing CAM to do all the machining on the Pocket NC v2-50. For this part I didn’t necessarily need any challenging workholding and since I could get the stock in tube form, there wasn’t an inordinate amount of material to remove either.
The one challenge is that when mounted in the Sherline Chuck, the mill can’t actually reach all the way to the edge of the part without hitting the X travel limit (which is why most of the other 100mm diameter parts I do are fixtured slightly off-center). In this case I tackled the problem in two iterations.