Tag Archives: spi

Bringing up CAN on the quad pi3 hat

After getting the power to work, the next step in bringing up the new quad’s raspberry pi interface board is getting the FDCAN ports to work.  As described in my last roadmap, this board has multiple independent FDCAN buses.  There are 2 STM32G4’s each with 2 FDCAN buses so that every leg gets a separate bus.  There is a 5th auxiliary bus for any other peripherals driven from a third STM32G4.  All 3 of the STM32G4’s communicate with the raspberry pi as SPI slaves.

Making this work was straightforward, if tedious.  I designed a simple SPI based protocol that would allow transmission and receipt of FD-CAN frames at a high rate in a relatively efficient manner, then implemented that on the STM32s.  On the raspberry pi side I initially used the linux kernel driver, but found that it didn’t give sufficient control over hold times during the transmission.  Since the SPI slave is implemented in software, I needed to leave sufficient time after asserting the chip select and after transmitting the address bytes.  The kernel driver gives no control over this at all, so I resorted to directly manipulating the BCM2837s peripheral registers and busy loop waiting in a real time thread.

dsc_0347
A lot of scope probes!

After a decent supply of bugs were squashed, I got to a point where the host could send off 12 queries to all the servos with the four buses all being used simultaneously, then collating the responses back.  I haven’t spent much time optimizing the cycle time, but the initial go around is at around 1.0ms for a query of all 12 devices which is about 1/3 of the 3.5ms I had in the previous single-bus RS485 version.

20200226-leg-transaction

Here’s a scope trace of a full query cycle with 2 of the 4 CAN buses on the top, and the two chip selects on the bottom.  Woohoo!

Bringing up the IMU on the pi3 hat

The next peripheral to get working on the quad’s raspberry pi interface board is the IMU. When operating, the IMU will primarily be used to determine attitude and angular pitch and roll rates.  Secondarily, it will determine yaw rate, although there is no provision within the IMU to determine absolute yaw.

To accomplish this, the board has a BMI088 6 axis accelerometer and gyroscope attached via SPI to the auxiliary STM32G4 along with discrete connections for interrupts.  This chip has 16 bit resolution for both sensors, decent claimed noise characteristics, and supposedly the ability to better reject high frequency vibrations as seen in robotic applications.  I am currently running the gyroscope at 1kHz, and the accelerometer at 800Hz.  The IMU is driven off the gyroscope, with the accelerometer sampled whenever the gyroscope has new data available.

My first step was just to read out the 6 axis values at full rate to measure the static performance characteristics.  After doing that overnight, I got the following Allan Variance plot.

20200304-bmi088-allan-variance

That gives the angular random walk at around 0.016 dps / sqrt(Hz) with a bias stability of around 6.5 deg/hr.  The angular random walk is about what is specified in the datasheet, and the bias is not specified at all, but this seems really good for a MEMS sensor.  In fact, it is good enough I could probably just barely gyrocompass, measuring the earth’s rotation, with a little patience.  The accelerometer values are shown there too, and seem fine, but aren’t all that critical.

Next up is turning this data into an attitude and rate estimate.