Next Halloween, my setup will include three coordinated skeletons performing together (probably doing “King Tut“). I want to use a single PIR motion detector to trigger three different props at different offsets from the original trigger. And some of the props can only speak or move for short amounts of time, so for a longer performance, they need to be repeatedly triggered. To do this, I have a PIR providing input to a Raspberry Pi Zero W. A python program running on the Pi then sends brief output trigger voltages individually to each of the props, according to the preset schedule for the routine. This same approach could easily be modified to trigger 2, 4, 5, or more coordinated props from a single start trigger. The hardware setup is very simple, and is shown in the figure.
Wiring diagram
Power is provided via the barrel jack on the prototype board. This is also what powers the Pi. Their are four 3-pin female headers on the board. The one on the left is for the PIR sensor input. It has the power and ground connections, and the signal wire is an input that goes to GPIO pin 15 on the Pi. The other three headers are to go out to the three props. The grounds are connected so that the prop controls and this trigger share a common ground. The signal connections are outputs from GPIO pins 23, 24, and 25. There is no need for power for these connections.
In order to test the hardware, I rigged up one LED to each of the three signal outputs, put a resistor in to avoid burning out any of the LEDs, and linked the grounds. The test setup is shown below below:
Test setup to make sure the hardware works and that I got the soldering correct.
I used the GPIO Zero library to write a simple test script for this test setup:
from gpiozero import LED
from gpiozero import MotionSensor
myLED1 = LED(23)
myLED2 = LED(24)
myLED3 = LED(25)
pir = MotionSensor(15)
while True:
if pir.wait_for_motion():
print("motion detected")
myLED3.off()
myLED1.blink(1)
myLED2.blink(2)
pir.wait_for_no_motion()
print("no motion")
myLED1.off()
myLED2.off()
myLED3.blink(3)
This has the pi wait until the PIR detects motion. Then it begins blinking the first 2 LEDs at different rates. When motion stops, those two LEDs are turned off and the third LED begins to blink. This cycle then repeats until the user hits CTRL-C to stop the test script.
Completed electronics in project box
Completed project box with electronics inside and lid on the top
The circuit is mounted in a custom 3d printed project box with slots for the power, sensor, and output wires, as well as a slot for accessing the Pi’s micro SD card. I designed the box using TinkerCad. I also 3d printed the standoffs, and just used hot glue to glue the Pi and the breadboard in place. I put in large holes so that it would be easy to plug and unplug the connectors and also get my fingers in to insert or remove the micro SD card. The top has a large cutout to make it easy to access the 3-pin headers. The finished hardware is shown in the figures on the right.
I’m in the process of integrating wireless audio input and into my Yorick the Mimic project. This involves adding microphone input and wireless transmission into the sensor cap and then integrating the movement controller with a modified version of Chatter Pi that takes the transmitted audio as an input.
In order to get started, I first put together bare bones transmitter and receive programs. I’m using Python, along with PyAudio, which I also used in Chatter Pi, to process the audio on both ends. I’m using UDP to send the data packets contain the audio. I saw some examples using TCP, but it seemed to me that UDP was better suited to real-time audio. If anyone knows more on which is the better approach, please post a comment.
PyAudio
The code runs fine, but generates a continuing stream of
warning messages. This doesn’t interfere with the program’s operations, but if anyone knows why I’m getting them and/or how to eliminate the warnings, I’d appreciate your letting me know.
PyAudio has two modes, a blocking mode, where each call to pyaudio.Stream.write() or pyaudio.Stream.read() blocks until all the given/requested frames have been played/recorded and a non-blocking mode where a callback function is launched in a separate thread, so that processing can continue in the program calling it, and the thread ends when the current chunk of audio is processed. The gist with my code uses the non-blocking mode using the callback function and two different versions of the receiver, one using blocking mode and one using non-blocking. You need to be careful when using the non-blocking mode that the callback function does not include anything really time consuming, like file reading and writing. If it does, it can’t finish before the next chunk of audio is ready and you get clipping or worse problems.
As is well-known, the audio jack output on a Pi produces low volume, poor quality audio. A USB speaker works much better. However at least for the speaker I’m using, I need to use the ALSAMixer to control the volume. If I touch the speaker icon in the GUI, it produces no sound if set to anything other than the maximum volume. Again, if anyone knows why and how to fix this, please add a comment.
By design, both the xmit and rcv programs run forever once started. There’s one other feature beyond the bare bones basics. The receive program has a Boolean variable named EFFECTS. If set to True, the Sox library is used to deepen the pitch and add a bit of reverb before sending the audio to the speaker.
Hopefully this project will help others with similar needs.
The sensor cap and the skull it wirelessly controls.
A few years back I presented my Yorick Alexa project at a local Hack & Tell meeting. At the meeting, someone asked if I could hook up a microphone to provide the voice, rather than Alexa. That is a pretty trivial change (much easier than getting Alexa interfaced in the first place), but from that I had the idea of capturing a person’s head motions for the skull in real-time, rather than using pre-canned, repeated routines. This Mimic project was the result.
This project uses two Raspberry Pi’s, communicating over WiFi using XMLRPC, along with an Inertial Measurement Unit (IMU) sensor and a Maestro Servo Controller to have a 3-axis skull wirelessly mimic the head movements of the operator, who is wearing a baseball cap with the sensor Pi and IMU unit mounted on the brim. (A shoutout to Greg G on Haunt Forum for the ball cap mount idea).
Side note: My first job out of college was as a systems engineer on the Shuttle Mission Simulator, including the simulation code for the rate gyro assemblies and the on-board accelerometers. There, I first heard of this weird thing called a “quaternion.” Now, over 40 years later, here I am using them in a hobby project!
Overview
This project has two units, a sensor unit and a controller unit. The sensor unit consists of a Raspberry Pi Zero W and an Adafruit BNO055 9 degree of free
The project has two units: the sensor unit and the controller unit. The Sensor unit consists of a Raspberry Pi Zero W and an Adafruit BNO055 9-Degrees of Freedom IMU board. The controller unit consists of another Pi Zero W and a Pololu Maestro Servo Controller. The two communicate with each other using XMLRPC running over WiFi. The Sensor unit acts as the server and the Controller unit acts as the client.
I mounted the sensor Pi and IMU board on the brim of a baseball cap, so it will track my head movements.
The IMU board has a triaxial accelerometer, a triaxial gyroscope, and a triaxial magnetometer. The magnetometer provides orientation relative to magnetic north and is not used in the IMU only mode used in this project, as only relative orientation to the starting position (which should be looking straight forward and level) is desired. The accelerometers and gyroscope. The secret sauce in this board is that it includes a high speed ARM Cortex-M0 based processor that takes in the raw data from all the sensors, fuses it, and outputs the calculated orientation in real-time.
The orientation is provided as Euler angles (think roll, pitch, yaw rotations about the board’s x, y, and z axes), as the x, y, and z components of the gravitational force vector, or as a quaternion (which defines the direction of a single rotation axis and the amount of rotation about that axis). Unfortunately the chip has some flaws in calculating Euler angles, so it’s safer to use the quaternion output. For this project, the aviation convention is used for angles (x axis looking forward, y axis pointing right, and z axis down). Note however, that some of the servos move in the opposite direction, so you’ll see some sign changes in the function that converts the angles to actual servo commands).
For moving the skull, we want the Euler angles, though, to command the tilt, nod, and turn servos of the skull. Fortunately it’s easy to find the math to implement to convert the quaternion value provided into the proper Euler angles.
In this project, the sensor Pi is set up to be an XMLRPC server which, when receiving the appropriate XMLRPC request, queries the board for the current orientation, provided as a quaternion, and wirelessly sends this to the controller Pi that made the request.
The controller Pi makes this request 50 times per second. Once it has the quaternion, it converts it to the three Euler angles, and then converts each angle into the appropriate servo command to pass on to the maestro.py module that communicates with the Maestro servo controller. In addition, there is a subroutine that sends commands to move the eyes servo in a pre-determined, but relatively random, fashion.
This video [[[[[insert] shows the system in action. The goal is not to get perfectly synchronized motion between the operator and the skull, as the servos will always introduce significant delay. Rather, the intent is to generate spontaneous and realistic movements by capturing the motion of the operator, who normally will be out of sight.
Software Prerequisites
The sensor software uses CircuitPython and Adafruit’s Blinka library must be installed to support CircuitPython on a Pi. In addition, the adafruit_bno055 program is needed to interface with the board. Be sure to use the CircuitPython version rather than the earlier version.
Hardware
Closeup of the BNO055 sensor and Pi Zero sensor unit
This project requires two Raspberry Pi’s with WiFi (I used Pi Zero W’s), an Adafruit BNO055 IMU board, and a Pololu Maestro Servo Controller to control the motions of a 3-axis skull. You also need some jumper wires and a USB OTG cable. The OTG cable is used to connect the controller Pi to the Maestro Servo Controller.
Sensor
The sensor unit captures the operator’s head movements. The sensor software is just one program, imu_rpc_server.py. It is configured to use a UART interface to the BNO055 board. One can use an i2c interface instead by changing a few line at the top of the program, however all Raspberry Pi’s have a hardware issue with i2c clock stretching, and the sensor board uses clock stretching. This gave extremely unreliable results when I was developing this software and I put the entire project aside for months until I came back and found out about the hardware issue. There is a workaround that you can use if you want to use i2c that consists of slowing the speed down so that clock stretching will rarely (hopefully never) be needed. You can read more about the issue here: https://www.mcgurrin.info/robots/723/.
Normally when the Pi kernel boots up it will put a login terminal on the serial port. You’ll need to turn this off if using the UART interface. To do so, you can run the raspi-config tool and go to Interface Options, then to Serial Port, and disable shell messages on the serial connection. Then reboot the Pi. If you later need to re-enable it, just follow the same procedure. To wire up the sensor board and the Pi using the UART interface:
Connect BNO055 Vin to Raspberry Pi 3.3V power
Connect BNO055 GND to Raspbery Pi ground
Connect BNO055 SDA (now UART TX) to Raspberry Pi RXD pin
Connect BNO055 SCL (now UART RX) to Raspberry Pi TXD pin
Connect BNO055 PS1 to BNO055 Vin / Raspberry Pi 3.3V power
You can test that everything is working by running the simpletestuart.py program. It will print out the temperature of the board, the individual sensor parameters, and the integrated Euler angle and Quaternion values, as well as the calibration status and the axis map. This will update every 5 seconds. Don’t worry that the magnetometer readings will be None. This project uses relative orientation and therefore the software turns off the magnetometer. For more information on the board and the various outputs and settings, see the datasheet.
The imu_rpc_server.py program is similar to the test program, but it only reads the quaternion values (the Euler angles are unreliable on this sensor). It also starts and XMLRPC server that will respond to read_sensor requests by returning the quaternion value for the current orientation. The program, once started runs continually until forced closed.
Controller
The controller software runs on a different Pi, and consists of two programs: main_controller.py and maestro.py. Main_controller.py initializes the servo controller and sets limits on the speed and acceleration for the servos. I found this cuts down on the noise of the servos, provides smoother motion, and keeps the head from whipping around when turning.
The board queries the sensor Pi via XMLRPC ten times per second to get the position as a quaternion. It converts the quaternion into Euler angles and converts the Euler angles into servo commands and sends the servo commands to the Maestro board. It also generates a sequence of eye movements and sends that to the Maestro as well.
ManServoTest can be used as you set things up to make sure your controller Pi is talking to the Maestro correctly.
Use
Set up the Pi’s to connect to your local WiFi network and install CircuitPython on the sensor Pi (it’s not needed for the controller). Then install the appropriate modules on each one. Connect the sensor Pi to the BNO055 and connect the controller Pi to the Maestro Servo Controller using a USB OTG cable. And, of course, connect the appropriate pins on the servo controller to the servos controlling your skull. Roll or tilt is channel 0, pitch or nod is channel 1, yaw or pan is channel 2, and random eye movements are sent out on channel 3.
Once everything is hooked up, you must start imu_rpc_server.py on the sensor pi first, so that it starts the XMLRPC server. Then launch main_controller.py on the controller Pi and you’re off and running.
Opportunities for Expansion
It would be very straightforward to take the sensor software and use it to capture and record head motions to a file. Then a different program could read back the file and send out the previously captured motion commands.
A somewhat more complex undertaking would be to capture the motions as described above, but then translate them into the scripting language used on the Maestro servocontroller, so that they could be played back without a Pi or other computer. Sending out 50 commands per second, however, would quickly fill the limited memory of the Maestro. Instead, one would want to pre-process the motion file so that only key commands are kept (analogous to keyframe animation) and only those key commands translated to the Maestro’s scripting language.
