By Arijit Das
SPY-DER
A 3D-PRINTED DIY ROBOT
SPY-DER is a speech and web-controlled surveillance spider robot. It
walks like a spider and acts as a spy using its camera, hence the name
‘SPY-DER’. The best aspect of it is that you can make it yourself using
some 3D-printed parts, a bunch of servos and some low-cost off-theshelf electronic modules.
Y
ou can control this robot in two ways — using
voice commands or its web-based control interface.
For example, I have nicknamed mine ‘Bumblebee’.
Whenever I call it by that name, it starts listening to me,
and it will then act on voice commands. I am using two
main technologies to enable this: hot-word or wake-word
detection and speech recognition.
The speech recognition also involves intent detection,
so that I can give it the same command in different ways.
For example, if I say ‘wave your hands’ or ‘say hello’, either
way, it will wave its legs.
For the web-control part, one can simply open a particular URL in any browser and use it to control the SPY-DER.
The web-based interface contains all the control options
as buttons. You can open another URL to watch the live
video feed from this robot’s camera.
You can see a short demonstration video that shows what
SPY-DER can do at https://youtu.be/3edXTxIZ_2U
Developing SPY-DER
Initially, I built a simple Bluetooth-controlled spider robot
using an Arduino Nano, but it could only be controlled
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using an Android or iOS app. Thus, I added speech recognition, web control and surveillance features.
Implementing all these features using an Arduino
was impossible – what I needed was a small computer.
That’s why I decided to add the Raspberry Pi Zero. In
theory, the whole system could have been implemented
using just the Raspberry Pi Zero, but it would have been
too time-consuming to rewrite all the spider movement
control code.
So I decided to keep the Arduino and add the Raspberry
Pi and have them communicate over a serial link. The
Arduino controls all the spider’s movements while the
Raspberry Pi sends commands to the Arduino. This also
means that I don’t have to worry about the Raspberry Pi
being so busy doing speech recognition that it loses control of the limbs!
All the Raspberry Pi code is written in Python. For the
web-based control part, I used the Flask framework and
built the web page using HTML, CSS and jQuery. For the
live video streaming, I used RPi-Cam-Web-Interface (see
https://elinux.org/RPi-Cam-Web-Interface) because it has
very low latency.
Practical Electronics | August | 2023
Fig.1: this diagram shows all the wiring
required for the SPY-DER robot. The
order in which the servos are connected is
important; see Fig.2, and note that the wire
colour coding can vary between models.
Also, be careful to check the labelling on the
other modules as they might not precisely
match what we’ve shown.
For speech recognition and hot word detection I used
Picovoice (https://picovoice.ai/) and modified the code in
Python. I tried local speech recognition, but the RAM and
processing power of the Raspberry Pi Zero is limited, so the
accuracy was not that good, and the latency was also high.
The physical robots parts are based on an existing robot
that I found at thingiverse.com/thing:2901132 (but it has
since been removed).
I redesigned some parts in TinkerCAD (www.tinkercad.com)
and made all of the relevant parts available online here:
thingiverse.com/thing:4815137
I 3D-printed all of the parts using an Ender 3 3D printer
(see Photos 1 and 2).
Starting assembly
If you’d like to watch a video, I have made one (just
over one hour long) going over the project in detail at:
https://youtu.be/KkZiZggtvIU
It is definitely worth watching before you start assembly.
And do see the parts list later in the article for what you
will need to build the SPY-DER.
I have created another video just under 30 minutes long
that concentrates on the steps for building SPY-DER, which
you can view at https://youtu.be/fnMmnd9k6q8
Practical Electronics | August | 2023
Step 1 – 3D print the parts
First, if you haven’t already done so, print all the 3D parts
that make up the robot. The STL files are available for
download from the August 2023 page of the PE website:
https://bit.ly/pe-downloads
Step 2 – attach the servo motors
Next, you need to attach the twelve SG90 servo motors
using M2 screws, as shown in Photos 3 and 4. Four of the
12 servo motors connect to the body while the other eight
connect to the legs. Attach them with screws, but don’t
add the ‘horns’ yet.
Plastic gear servo motors are used for this project as the robot
is pretty light. I have provided some useful details on attaching the servos, along with Steps 3, 4, 5 and 6, in the video at:
https://youtu.be/fnMmnd9k6q8
Step 3 – join the body parts
Next, connect all the 3D-printed body parts through the
servo motors – see Fig.2. Don’t attach the horns just yet.
Step 4 – connect the battery and BMS
The power requirements of the 12 servo motors is quite high,
so I used two 18650 Li-ion cells in series. The Arduino, servo
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motors and Raspberry Pi use a 5V DC supply. An LM2596 buck
converter is used to convert the 7-8V output of the battery to
a regulated 5V, which is then fed to all the components. For
safety, a battery management system (BMS) is used.
Fig.1 shows how these parts are connected, including
some other parts we’ll get to shortly.
Make sure that when you join these, it can still fit within
the robot’s body, as inserting it is the next step. Photo 5
shows how I wired these parts up (including the on/off toggle switch), while Photo 6 shows it installed in the robot
body. Note how the servo power/control leads have been
fed into the main cavity.
GitHub repository. After uploading this, when you power
the robot up, it will automatically start testing all the features in the following order:
• Stand up
• Move forward
• Move backwards
• Move left
• Move right
• Hand wave
• Dance
Step 5 – setting up the Arduino
First, plug the Arduino Nano into the socket on the Prototype
Shield – make sure it’s the right way around. Next, plug all
the servo motors into the headers on the I/O shield. While
attaching the servo motors, make sure you have attached
them according to the numbering shown in Fig.1 and Fig.2,
with the black wires to the side marked ‘G’ (for ground).
The I/O shield also needs to be wired up to the power
supply which powers the servo motors and the Arduino.
Make sure the power switch is off when you connect it.
Photo 7 shows what the Nano looks like once placed
inside the robot’s body.
Step 6 – servo calibration
Now, upload the code to the Arduino Nano. The code is
available to download from https://bit.ly/pe-aug23-spy4
– it’s also available from the August 2023 page of the PE
website: https://bit.ly/pe-downloads
The first step is to calibrate the robot legs. The program
to do this is in the ‘Legs’ folder (legs.ino). Before calibrating the servo motors, check the connections are correct and
appropriately powered. After running the legs.ino calibration sketch, screw the horns that hold the legs to the body.
Any deviations from the above movements need to be
checked as they suggest an incorrect connection or component that is not working correctly. To know more about
this and the last step, do watch my videos.
Step 8 – uploading the final Arduino code
Now you can upload the final code to the Arduino. This
will work with the Raspberry Pi. Lke the The code is available from https://bit.ly/pe-aug23-spy5 (and the PE website).
Upload the SPY-DER_Arduino.ino file to the Arduino. This
program takes commands from the Pi and acts accordingly.
Step 9 – preparing the Raspberry Pi
Start by installing the latest version of the Raspbian operating system on the Raspberry Pi. You can use SSH or a direct
HDMI connection while working with the Raspberry Pi.
Step 10 – Raspberry Pi microphone and camera
To check the basic functionality of the robot, there is another
Sketch named program1.ino in the program1 folder of the
The Raspberry Pi needs to have the mic, camera and logic
level shifter attached, as shown in Photo 8.
This logic level shifter is needed to interface the 5V Arduino with the 3.3V I/Os on the Raspberry Pi. The Arduino
connects to the 5V (‘HV’) side of the level shifter, while
the Pi goes to the 3.3V (‘LV’) side.
I have a general video about using a level shifter like
this for serial communication between different boards at:
https://youtu.be/e04br5J4UpQ
To connect a microphone to the Raspberry Pi Zero, there
are three options:
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Step 7 – initial functional testing
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Practical Electronics | August | 2023
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1. Connecting a USB microphone using an OTG cable
2. Connect a microphone with a 3.5mm jack plug using a
Raspberry Pi sound card and OTG cable
3. Using a Raspberry Pi audio hat.
Clone the Picovoice (https://picovoice.ai/) repository
from https://github.com/Picovoice/picovoice and then
launch the Picovoice program in my GitHub repository
(see the README file).
Install RPi-Cam-Interface for video streaming. You can get
it from: https://elinux.org/RPi-Cam-Web-Interface and see
the video at: https://youtu.be/yzpqEw1kEGo for more details.
Train the Rhino speech-to-intent model so that for a single task, you can use different commands; Rhino is contained in the Picovoice repository.
To train the model, open a web browser and then go to:
https://console.picovoice.ai/rhn and input different kinds
of commands and their intentions – see Screen 1. Depending on the intentions you use here, you need to change the
picovoice_demo_mic.py file.
After writing down all the commands and intents, follow the prompts on the webpage to train the model by
using the microphone, then upload the trained model to
the Raspberry Pi.
For web control, you need to install the Flask framework in Python; all of the Python and HTML files are in
my repository.
I suggest you connect a USB microphone using an OTG
cable as I did. The Raspberry Pi supports most standard
USB microphones.
For the camera, use a standard Raspberry Pi camera
– www.raspberrypi.com/products/camera-module-v2 –
and plug it in as per the instructions. I have a video on
using the Raspberry Pi Camera with a Raspberry Pi Zero at:
https://youtu.be/oo0A_yRrIxQ
Step 11 – setting up the Raspberry Pi
The remaining setup steps are as follows:
1. Set up VNC Connect on the Raspberry Pi so that you can
remotely access and control it from your computer.
2. Switch on the camera in the settings or use raspi-config
from the command line. Check that the camera works;
RaspiStill can be used to test it.
3. Enable the microphone and then test recording from the
terminal. You might need to modify the .asoundrc file
to set up the mic.
4. Test serial communications between the Pi and Arduino.
Clone all the code from my GitHub repo (see:
https://bit.ly/pe-aug23-spy1) onto the Raspberry Pi (say,
into the home folder).
Step 12 – finishing the build and controlling the robot
Fit everything inside the body (Photo 9) and glue the
microphone and camera into the holes provided in the lid
(Photo 10). Attach the lid, power it up and then use VNC to
connect to the Raspberry Pi wirelessly from your computer.
Photo 11 shows the completed robot with the lid attached.
Fig.2: match these servo
numbers up with the
connections shown in Fig.1.
Practical Electronics | August | 2023
35
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To start the web control interface, open a console inside
the SPY-DER GitHub repository root folder and enter the
following commands:
You can choose any of the keywords to use as your robot’s
wake word.
Finally, you need the speech-to-text model, which you
have already trained and downloaded. Then you can run
the code with these two files using the following commands:
cd web_control
python3 web_control.py
After running these commands, you can access the web
control interface from any browser using the URL
http://<raspberry_pi_ip_address>:5010 (insert the current IP address of your Raspberry Pi) – see Screen 2. From
here, the robot can be controlled using all those buttons.
You can modify the control interface by changing the code
in the web_control folder.
Step 13 – Speech control
Go into the picovoice folder to run the speech control system. There are three files there you will need. The first one is
the main code file named picovoice_demo_mic.py. Modify
this code according to your speech-to-text model training.
The next file needed is the porcupine keyword file. This
is the keyword that you will use to call the robot. There are
many pre-trained files available in the Picovoice repository.
cd picovoice
python3 demo/python/picovoice_demo_mic.py \
--keyword_path resources/porcupine/resources/
keyword_files/raspberry-pi/bumblebee_raspberrypi.ppn \
--context_path your_rhino_model
In this example, I have used bumblebee_raspberry-pi.ppn
as the keyword file, so ‘bumblebee’ is the wake word for
my robot.
Step 14 – Video streaming
You can enable live video streaming either using voice
commands or the web control interface. After turning on
the live video surveillance, to access it, open the URL
http://<raspberry_pi_ip_address>:80 in a web browser.
Conclusion and future improvements
There is plenty of room for modifications to this
project. For example, if a local speech recognition system could be designed that would perform
well on a Raspberry Pi, that would speed up its
response to voice commands and remove the need
for an Internet connection.
Snow-boy hot-word detector is an open-source
hot-word detector that works pretty well on the
Raspberry Pi. It provides several image processing features like object detection – for example,
face recognition. It could potentially be added to
this project. Maybe I will upgrade it in the future!
Reproduced by arrangement with
SILICON CHIP magazine 2023.
www.siliconchip.com.au
►
Screen 1: the Picovoice Rhino
training console. Here you
can teach it how you say the
different words that you will
later use to control the robot.
You’ll need to sign up for
an account on the Picovoice
website to allow you to do this.
Screen 2: the SPY-DER web ►
control interface is quite simple,
and all the functions of the
buttons are pretty obvious. This
works in parallel with voice
control, assuming you have
voice control up and running.
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Practical Electronics | August | 2023
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3D printed robot parts – STL 3D printer files available
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