Daniel Hingston wanted to build a four-legged walking robot for several years, and with current coronavirus restrictions he finally got his chance. His 3D-printed robodog, dubbed “GoodBoy,” is reminiscent of a miniature version of Boston Dynamics’ Spot, which helped inspire the project.
It’s extremely clean, with wiring integrated into the legs mid-print. Two micro servos per leg move it in a forward direction, controlled by an Arduino Uno.
Obstacle avoidance is provided by a pair of ultrasonic sensor “eyes,” allowing it to stop when something is in its path. An LDR sensor is also implemented, which when covered by its human minder commands it to present its paw for shaking.
Be sure to check out a short demo of GoodBoy below!
What is one to do when stuck indoors due to bad weather or other circumstances, without the ability to ride your beloved bicycle? If you’re game designer Jelle Vermandere, you build your own cycling simulator as seen in the clip below.
Vermandere not only created a computer simulation in Unity, but a custom Arduino Uno rig that allows him to use his actual bike as the controller.
The game features procedurally-generated maps, along with competitors using Vermandere’s own likeness scanned in as the model. When the racing begins, wheel speeds are sensed via a magnetic window sensor and steering is handled by a LEGO potentiometer rig.
This post is written by Jan Jongboom and Dominic Pajak.
Running machine learning (ML) on microcontrollers is one of the most exciting developments of the past years, allowing small battery-powered devices to detect complex motions, recognize sounds, or find anomalies in sensor data. To make building and deploying these models accessible to every embedded developer we’re launching first-class support for the Arduino Nano 33 BLE Sense and other 32-bit Arduino boards in Edge Impulse.
The trend to run ML on microcontrollers is called Embedded ML or Tiny ML. It means devices can make smart decisions without needing to send data to the cloud – great from an efficiency and privacy perspective. Even powerful deep learning models (based on artificial neural networks) are now reaching microcontrollers. This past year great strides were made in making deep learning models smaller, faster and runnable on embedded hardware through projects like TensorFlow Lite Micro, uTensor and Arm’s CMSIS-NN; but building a quality dataset, extracting the right features, training and deploying these models is still complicated.
Using Edge Impulse you can now quickly collect real-world sensor data, train ML models on this data in the cloud, and then deploy the model back to your Arduino device. From there you can integrate the model into your Arduino sketches with a single function call. Your sensors are then a whole lot smarter, being able to make sense of complex events in the real world. The built-in examples allow you to collect data from the accelerometer and the microphone, but it’s easy to integrate other sensors with a few lines of code.
Download the Arduino Nano 33 BLE Sense firmware — this is a special firmware package (source code) that contains all code to quickly gather data from its sensors. Launch the flash script for your platform to flash the firmware.
Launch the Edge Impulse daemon to connect your board to Edge Impulse. Open a terminal or command prompt and run:
Your device now shows in the Edge Impulse studio on the Devices tab, ready for you to collect some data and build a model.
Once you’re done you can deploy your model back to the Arduino Nano 33 BLE Sense. Either as a binary which includes your full ML model, or as an Arduino library which you can integrate in any sketch.
Deploying to Arduino from Edge Impulse
Your machine learning model is now running on the Arduino board. Open the serial monitor and run `AT+RUNIMPULSE` to start classifying real world data!
Keyword spotting on the Arduino Nano 33 BLE Sense
Integrates with your favorite Arduino platform
We’ve launched with the Arduino Nano 33 BLE Sense, but you can also integrate Edge Impulse with your favourite Arduino platform. You can easily collect data from any sensor and development board using the Data forwarder. This is a small application that reads data over serial and sends it to Edge Impulse. All you need is a few lines of code in your sketch (here’s an example).
After you’ve built a model you can easily export your model as an Arduino library. This library will run on any Arm-based Arduino platform including the Arduino MKR family or Arduino Nano 33 IoT, providing it has enough RAM to run your model. You can now include your ML model in any Arduino sketch with just a few lines of code. After you’ve added the library to the Arduino IDE you can find an example on integrating the model under Files > Examples > Your project – Edge Impulse > static_buffer.
To run your models as fast and energy-efficiently as possible we automatically leverage the hardware capabilities of your Arduino board – for example the signal processing extensions available on the Arm Cortex-M4 based Arduino Nano BLE Sense or more powerful Arm Cortex-M7 based Arduino Portenta H7. We also leverage the optimized neural network kernels that Arm provides in CMSIS-NN.
A path to production
This release is the first step in a really exciting collaboration. We believe that many embedded applications can benefit from ML today, whether it’s for predictive maintenance (‘this machine is starting to behave abnormally’), to help with worker safety (‘fall detected’), or in health care (‘detected early signs of a potential infection’). Using Edge Impulse with the Arduino MKR family you can already quickly deploy simple ML based applications combined with LoRa, NB-IoT cellular, or WiFi connectivity. Over the next months we’ll also add integrations for the Arduino Portenta H7 on Edge Impulse, making higher performance industrial applications possible.
On a related note: if you have ideas on how TinyML can help to slow down or detect the COVID-19 virus, then join the UNDP COVID-19 Detect and Protect Challenge. For inspiration, see Kartik Thakore’s blog post on cough detection with the Arduino Nano 33 BLE Sense and Edge Impulse.
We can’t wait to see what you’ll build!
Jan Jongboom is the CTO and co-founder of Edge Impulse. He built his first IoT projects using the Arduino Starter Kit.
Dominic Pajak is VP Business Development at Arduino.
Embodied Axes is an Arduino-powered controller for 3D imagery and data visualizations in AR
Arduino Team — May 25th, 2020
Researchers across several universities have developed a controller that provides tangible interaction for 3D augmented reality data spaces.
The device is comprised of three orthogonal arms, embodying X, Y, and Z axes which extend from a central point. These form an interactive space for 3D objects, with linear potentiometers and a rotary button on each axis as a user interface.
At the heart of it all is an Arduino Mega, which takes in data from the sliders to section a model. This enables users to peer inside of a representation with an AR headset, “slicing off” anything that gets in the way by defining a maximum and minimum view plane. The sliders are each motorized to allow them to move together and to provide force feedback.
This robot looks like a ball and transforms itself into a quadruped to move
Arduino Team — May 25th, 2020
Gregory Leveque has created an adorable 3D-printed robot that not only walks on four legs, but folds up into a ball when not in use.
To accomplish this, the round quadruped utilizes one servo to deploy each leg via a parallelogram linkage system and another to move it forwards and backwards. A clever single-servo assembly is also implemented on the bottom to fill gaps left by the legs.
The device is controlled by an Arduino Nano, along with a 16-channel servo driver board. Obstacle avoidance is handled via an ultrasonic sensor, which sticks out of the top half of the sphere and rotates side to side using yet another servo.
It’s an impressive mechanical build, especially considering its diminutive size of 130mm (5.12in) in diameter.
3D printing allows us to make a wide variety of shapes, but adding interactive features generally means somehow strapping various electronics to them. The AirTouch project, however, presents an alternative option by enabling a fabricated object to sense up to a dozen different touch points with no components or complex calibration necessary.
Instead compressed air is pumped into the 3D-printed item, which escapes via up to 12 tiny holes. As each hole is touched, a barometric sensor picks up the pressure response, which is then interpreted by an Arduino Uno board as user input.
The system has been tested on a variety of interactive figures, from a model rabbit to a bar graph. A short demo can be seen below, while the project’s research paper is found here.
Create your own crack the code game with an Arduino-controlled safe
Arduino Team — May 22nd, 2020
Michael Klements’ laser-cut, Arduino-controlled “safe” may not be the most secure model that you can find, but it certainly makes for a very entertaining puzzle.
Users can place whatever “treasure” they want hidden inside, shut the door, and press a rotary encoder button to lock it via a micro servo. They then must decipher a randomly generated four-digit code to get it open again.
Number guesses are input using the encoder dial on the front, which are displayed by a small OLED I2C screen. Green and red LEDs provide feedback as to how many digits are correct and if they’re in the right position, eventually letting users figure out where everything goes by a process of elimination.
This electronic foosball goal system will make score markers a thing of the past
Arduino Team — May 21st, 2020
Like most of us, Joey Cumeras Khan has been stuck inside for the last couple of months. As a foosball fan, this not only meant plenty of time to play with his fellow housemates but also the perfect opportunity to tech out his table with an automated scoring system.
Khan’s setup works by tracking goals via an LED and LDR pair situated in each player’s scoring tray. When a ball drops into the net, the score state is picked up by the sensor as a lack of light, thus signaling the controlling Arduino Nano that a goal has been made. This info is sent to an Orange Pi Zero, which runs a web app to display the current score on both players’ phones.
To get started, one simply has to scan a QR code on the side of the table. This prompts the scoreboard along with some custom sounds and a reset interface to enhance your game experience!
As an enviable senior design project at Kennesaw State University, students Tyler Gragg, Kevin Kamperman, Cody Meier, and Omar Salazar Lima created their own autonomous pinball machine.
As seen in the videos below, the table is not only nicely finished with an interesting playfield, but works quite well without human interaction.
The machine’s IO system is actuated using an Arduino Mega, with higher-level controls handled by a computer running the Robot Operating System. While the game can be enjoyed manually, for automatic operation an overhead camera tracks the moving ball and calculates its position. When within tunable “flip zones,” the correct flipper is triggered, (hopefully) flinging the ball forward for more points and mayhem!
If you’ve been following the development of the Arduino IoT Cloud closely, you have probably noticed that over the months the Dashboard features have been progressing by leaps and bounds.
Sure, behind the scenes there’s work being done every day, but our users need and want features that better help them manage their connected devices.
As Arduino moves towards a more cohesive UX and UI, we recently released a set of new widgets for our enhanced, aggregated Dashboard which allows users to pick from multiple IoT things and build beautiful control panels with lots of flexibility.
Here’s a quick summary video highlighting these new features and widgets.
This color-changing lamp emulates a “touch” sensor with a microphone
Arduino Team — May 18th, 2020
YouTuber “Peterbiglab” wanted an input for his DIY color-changing lamp, but didn’t want to use buttons or even have it detect claps, so he decided to make it respond to touch.
Instead of using a capacitive sensor or force-sensitive resistor, he placed a microphone inside his lamp to hear inputs. An op-amp was also included to pull the voltage up to a level that can be read by its Arduino Nano.
With code implemented to keep it from picking up on music and other sounds, the device can now be controlled with finger taps: two to switch colors, three to turn on/off , or even four to enter Hue mode. It’s an ingeniously-simple sensing method, which works well here, and could be applied to a variety of other projects.
Mech-Dickel Robotics has designed a beautiful quadruped robot dubbed mechDOG, which utilizes a dozen servos for motion. This gives each leg three degrees of freedom, allowing the cat-sized beast to travel a meter in 8.46 seconds. While it won’t break any speed records, creating a walking motion on this sort of unstable platform is an impressive feat in itself.
mechDOG is controlled by an Arduino Uno, while a Lynxmotion Smart Servo Adapter Board interfaces with the servos themselves. The device is remote-controlled via an RF unit, though it does have a pair of ultrasonic sensors that presumably could be used for obstacle avoidance.
You can check it out in action in the videos below, looking sharp in its yellow-finished aluminum sheet metal chassis.
Beat boredom with an Alexa-controlled, face-tracking candy launcher
Arduino Team — May 14th, 2020
As many of us are stuck at home these days due to COVID-19, Harrison McIntyre decided to do something fun and “make it rain candy.”
What he came up with was a build that uses a flywheel to launch M&M’s, — speed-regulated using magnets and a Hall effect sensor, and aimed with a few servos to control pitch and yaw. This allows it to fire in rapid succession, at an initial speed of 23 miles per hour.
The device is controlled by a pair of Arduino Nano boards along with a Raspberry Pi and USB camera. How it works is as follows: a Python script takes an image, runs facial detection, rotates the turret to point towards a face, does facial landmark detection to find the mouth, calculates the correct parabola, and finally tosses the candy. It’s even paired up with an Amazon Echo Dot that allows McIntyre to simply ask for chocolates via Alexa commands!
When working with Styrofoam, a conventional CNC machine like a router or laser cutter just won’t do. However, as shown in Michael Rechtin’s excellent video below, a hot wire can carve foam like butter, creating custom wings for RC aircraft or whatever else you can dream up.
Rechtin’s build uses a double gantry system to pull the wire in a horizontal and vertical directions. As each is mechanically independent of the other, they can move in tandem, or in a skewed orientation for cutting across both the horizontal and vertical planes.
Control is via an Arduino Mega running Grbl, with a RAMPS 1.4 shield.
Remote working with Arduino: Alexa and the Arduino IoT Cloud
Arduino Team — May 13th, 2020
We’ll certainly remember this year, with many of us learning how to adapt and live a safe life-style under the pandemic. As many countries begin to initiate a relaxation of restrictions and we are starting to be able to leave our houses, arrange shifts to get into work (still observing safety measures), we might find ourselves with different needs.
Perhaps we got used to having the TV on more often than usual, letting the voices of shows we don’t really watch keep us company in the background while home alone… working. Maybe we just like to have a corner light up with colors which soothe our moods, or give a fancy background tint to a remote disco party as we dress with big shades and wigs.
Many of these things can easily be automated using Alexa and Arduino IoT Cloud, so when we (finally) leave the house and are not sure if the TV is still on, or if our living room corner is still purple! we can simply ask “Alexa, turn the TV off” or “Alexa, turn the living room off” and a series of smart devices and software will take care of it for us.
Let’s give it a shot…
Getting the Alexa skill: Learn how to use Arduino IoT Cloud and Amazon Alexa to interact with your sensors.
With many years of practice and discipline, you too can improve your basketball skills. Or you could just choose to play with Shane Wighton’s automated backboard that sinks nearly any shot that you take.
The setup uses a Microsoft Kinect, along with a Windows-based computer to calculate the inbound ball’s trajectory. On approach it passes data to an Arduino, which then moves the hoop into position via three large steppers and a linkage system.
While this compensates for the “occasional” error, as shown in the excellent explainer/demo video below, you do have to actually hit the backboard for it to work!
Have a blast with this Arduino-powered laser shooting game
Arduino Team — May 12th, 2020
As a fun weekend project at home, Arnaud Atchimon and his son came up with a DIY laser blaster game comprised of a 3D-printed shooter and target setup.
Both devices are controlled by an Arduino Nano, and when the gun is powered up, you can either have 10 shots on target before a reload delay or go into “Stormtrooper” mode for full auto-blasting.
The target assembly senses hits with an LDR sensor and features a 24-LED NeoPixel ring that makes it easy to spot. When struck, it falls either forward or backwards under the power of a micro servo motor, allowing the unit to be used on a flat surface or mounted to the wall.
Please Leave A Message is an interactive plotter that spreads positivity
Arduino Team — May 11th, 2020
As Niklas Roy puts it, “Currently, times are weird.” It’s hard to argue with his sentiment and in order to add a bit more positivity to the world, or at least the area near his Berlin workshop, he created an interactive pen plotter for his window.
The idea is that while he didn’t know exactly what to say, Roy’s project — aptly named “Please Leave a Message” — allows passersby to create their own uplifting comments for other pedestrians to see over WiFi.
The device repurposes the mechanical bits of an old but solidly built Aritma Colorgraf plotter, which is now controlled by an Arduino Mega and new driver hardware. People log into the installation’s simply web server through a separate ESP8266 module, which sends text to be printed to the Mega via a serial connection. There’s even an RTC that notes the precise time and date of when each message was written.
In order to stop the spread of COVID-19, many of us are wearing face masks when outside of our homes. While this can be effective, these coverings can be hot, inhibit breathing, and certainly make eating and drinking a bit difficult. What if they could instead be applied only when others are around?
Chen the DesignMaker decided to test this concept out with a servo-actuated mask, which features a hatch that actuates based on input from three PIR sensors and an Arduino Nano.
When the sensors pick up movement, the mask closes; and when they aren’t activated for a period of time, it opens again. Notably, the device doesn’t have any sort of cloth or other sealing materials inside, making it very much a proof-of-concept, not an effective medical device.
BiTipText enables text entry across two index finger-worn keyboards
Arduino Team — May 5th, 2020
Typing with your thumbs on a smartphone has become an everyday activity for many, but what if you could enter text by simply tapping on your index fingers? With BiTipText, that may soon be a reality.
The researchers’ prototype consists of an interactive skin overlay made out of flexible PCB material, allowing an Arduino Uno and MPR121 sensor chip to read capacitive signals from both digits.
In testing, users were able to enter text at over 23 WPM, with a 0.03% uncorrected error rate. Notably, the two-handed implementation means that software can determine not only the position of presses, but the sequence of left/right inputs to help with word interpretation.
Hacker “replayreb” recently obtained some IKEA FYRTUR motorized blackout shades, but wasn’t satisfied with the stock remote control that comes with them. Instead he wanted to be able to open and shut the blinds with the same remote that he uses for AV equipment.
Rather than attempting to go through a home automation gateway setup, he simply opened up the remote and wired an Arduino Leonardo to use its GPIO pins as a low-voltage relay. An IR receiver was added to the Arduino, allowing it to take these signals and translate them into simulated button presses as needed.
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