Last time I discussed the rationale for building a custom control and telemetry solution.  Here I’ll describe the protocol design a little bit, before discussing the implementation in a future post.

Frame design and frequency hopping

The basic idea is that the transmitter sends a frame to the receiver every 20ms, and each frame is sent on a different radio frequency.  A set of frequencies and their order is generated pseudo-randomly based on a “key” that the transmitter and receiver each share ahead of time.  The receiver replies on the same frequency with its telemetry.  Then the transmitter and receiver each switch to the next frequency in the list to get ready for the next frame.

Now that I have both sides of the nrf24l01+ link covered, it was time to design a protocol to take advantage of them.

Design space

To recap, what I needed was a reliable means of commanding the robot and receiving telemetry, even in congested radio environments.  At competitions or events like Maker Faire, Robogames and such, the wireless environment is often totally trashed.  Hundreds of devices are operating in close proximity, across all spectrum bands, including plenty of things that probably aren’t licensed to be transmitting in the first place.  When we first built Super Mega Microbot, we used a custom protocol with a 5 GHz wifi transmitter as the physical layer and selected USB based dongles which allowed control over the physical layer.  USB proved problematic, and with national RF regulations, it is extremely challenging to find wifi devices which provide that level of control at the RF layer.  Also, even with full physical layer control, wifi is difficult to make work in a reliable manner as there is so much congestion in both the 2.4GHz and 5GHz bands and the channels are so wide.

Thankfully, I’m now at the point where I’m fixing actual dynamics problems on the robot.  Doubly thankfully I have a robot which is pretty robust and keeps working!  That said, it is still, shall we say, “non-ideal”, to be testing code for the first time ever on a real robot.

Back with my HerkuleX based Super Mega MicroBot, I had a working DART based simulation which was decently accurate.  However, the actuators for that machine were so limited that it didn’t really make sense to do any work in simulation.  The only way to be effective with that machine was to tweak and tweak on the real platform and rely on exactly the right amount of bouncing and wiggling that would get it moving smoothly.

While I was able to make the r2 power distribution board work, it did require quite a bit more than my usual number of blue wires and careful trace cutting.

Thus I spun a new revision r3, basically just to fix all the blue wires so that I could have some spares without having to worry about the robustness of my hot glue.  While I was at it, I updated the logo:

First, a limited number of qdd100 servos are available for sale to beta testers!  Check them out at mjbots.com.

After building up the first set of qdd100 servos, I wanted to empirically measure their performance parameters.  Some astute commenters uncovered in my terrible juggling video, that I didn’t actually have any ground truth measure of torque with these actuators.  Given that the ultimate torque is a pretty useful performance metric, it’s a good thing to have a solid understanding of.

In order to bring up the final piece of the raspberry pi 3 hat, the nrf24l01+, I wanted a desktop development platform that would allow for system bringup and also be useful as a PC side transmitter.  Thus, the nrfusb:

Similar to the fdcanusb, it is just an STM32G474 on the USB bus, although this has a pin header for a common nrf24l01+ form factor daughterboard.

The next steps here are to get this working at all, then implement a spread spectrum bidirectional protocol for control and telemetry.

After I restructured my control laws to take advantage of high rate force feedback for the pronking experiments, I haven’t actually managed to port the walking gait yet.  Now that I have a brand new robot, it seemed like a good time!

This gait is basically the same thing as I ran on the quad A0 in principle.  The opposing feet are picked up according to a rigid schedule, and moved to a point opposite their “idle” position based on the current movement speed.  Any feet that are completely placed on the ground just move with the inverse of the robot’s velocity.

After getting all the legs swapped out, I ran my existing software to validate that all the pieces worked together.  Here’s a quick video showing basically what I’ve shown before, but with all new hardware:

Now that I have a bunch of the mk2 servos set and ready to go, a new leg design, a new power distribution board to power them, and a raspberry pi3 hat to communicate with them, I built a new quadruped!  I’m calling this the mjbots quad A1, since basically everything is upgraded.

After I initially assembled the new legs onto the chassis, I realized I had the geometry slightly off and there was some interference through part of the shoulder rotation.  I made up new printed parts and replaced everything in front of the camera.  Thus, watch some high speed robot surgery:

Now that the IMU is functioning, my next step is to use that to produce an attitude estimate.  Here, I dusted off my [unscented Kalman filter](http://unscented Kalman filter) based estimator from long ago, and adapted it slightly to run on an STM32.  As before, I used a UKF instead of the more traditional EKF not because of its superior filtering performance, but because of the flexibility it allows with the process and measurement functions.  Unlike the EKF, the UKF is purely numerical, so no derivation of Jacobians is necessary.  It turns out that even an STM32 has plenty of processing power to do this for things like a 7 state attitude filter.