My initial design torque for the qdd100 was a little over 17 Nm. However, when I did my first ground truth torque testing, I found that some servos had a lower maximum torque than I had specified. While working to diagnose those, I built a qdd100 that used an alternate stator winding of 105Kv instead of the 135Kv that are in all the beta units. The Kv rating of a stator describes how fast the motor will spin for a given applied voltage. If you assume the same amount of copper mass of wiring, a lower Kv will mean that there are thinner wires that wrap around the stator more turns (or fewer wires in parallel). A higher Kv will have thicker wires with fewer overall turns.

For some upcoming work, I needed to drive the quad A1 around without being tethered to a computer. To date, my control mechanisms have been:

• A web interface driven from a computer with joystick attached.
• A nrf24l01 link driven from a nrfusb attached to a computer.

Note, both of those methods involve being tethered to a computer, which makes it hard to be mobile. As a possibly short term solution to this problem, I went ahead and got a bluetooth “gaming” controller for my phone (non-affiliate amazon link):

I first wrote about the very very simple web UI for the quad A1 some time ago. That UI served its purpose, although it was largely inscrutable to anyone but myself, as the primary data display was a giant wall of unformatted JSON which filled most of the page. It was also pretty easy to accidentally pick the wrong mode, cause the robot to jump instead of walk, or cut power to everything.

I’ve now built 3 or 4 complete quad A1 style robots depending upon how you look at it. Each was somewhat of a one-off, incrementally modified over time as I discovered failure modes and improved the design. Before starting to serially build quad A1 style robots, I wanted to get a better understanding of how much actually goes into making one. The quad A1 has a fair number of sub-assemblies, custom PCBs, harnesses, and assembly steps that go into its production. During previous builds, I kept running into problems where I would run out of some component, fastener, or raw material unexpectedly, then have to wait for its lead time before I could continue.

In my original series on balancing while walking, (part 1, part 2, part 3), I described some heuristics I used to handle changing directions. That was minimally sufficient in 1D, however in 2D it still leaves something to be desired, as there are more possible degenerate cases. The biggest is when spinning in place. There, the center of mass doesn’t really move at all relative to the balance line, but we still need to take steps!

Last time, I described my approach for estimating the terrain under the robot based on the inertial measurement unit and proprioceptive foot feedback. Now, I’ll cover how that is used to balance.

“R” Frame

First, let me explain the “R” or “robot” frame and how it is used. The frames I’ve discussed in this series so far are the “B” frame, which is rigidly attached to the center of the robot body, the “M” frame, which is located at the center of mass and level with the ground, and the “T” frame, which is under the robot and level with the current terrain.

Last time I discussed the challenges when operating the mjbots quad A1 on sloped surfaces. While there are a number of possible means of tackling this, the approach I’ve gone with for now is to estimate the slope of the terrain under the robot, and use that to determine how to position the center of mass. Here’ll I’ll cover the estimation part of this solution.

On paper, the quad A1 has plenty of information to estimate the terrain under its feet. Between the IMU with attitude estimator, the proprioceptive feedback from the joints, and the ability to move the feet around, it would be obvious to a human whether the ground under them was sloped or level. The challenge here is to devise an algorithm to do so, despite the noise in the IMU, the fact that the feet are not always on the ground, and that as the robot moves, the terrain under it changes.

Not too long ago, I ran some outdoor experiments, and while piloting the quad A1 around, realized that it wasn’t going to get very far if it was restricted to just flat ground.

Since the control algorithms are completely ignorant of slopes, the center of gravity of the machine can easily get too close to the support polygon when resting, and similarly fails to stay balanced over the support line during the trot gait.

Last time I covered the new software library that I wrote to help use all the features of the pi3hat, in an efficient manner. This time, I’ll cover how I measured the performance of the result, and talk about how it can be integrated into a robotic control system.

Test Setup

To check out the timing, I wired up a pi3hat into the quad A1 and used the oscilloscope to probe one of the SPI clocks and CAN bus 1 and 3.

The pi3hat r4.2, now in the mjbots store, has only minor hardware changes from the r4 and r4.1 versions. What has changed in a bigger way is the firmware, and the software that is available to interface with it. The interface software for the previous versions was tightly coupled to the quad A1s overall codebase, that made it basically impossible to use with without significant rework. So, that rework is what I’ve done with the new libpi3hat library: