Arduino Nano 33 BLE Rev2
Introducing the Arduino Nano 33 BLE Rev2, a cutting-edge development board powered by the nRF52840 from Nordic Semiconductors. With a 32-bit Arm® Cortex®-M4 CPU running at 64 MHz, this board boasts Bluetooth® LE capabilities for seamless data sharing with other Bluetooth® LE-enabled devices. Additionally, it supports MicroPython for enhanced flexibility in programming.
Overview
The Arduino Nano 33 BLE Rev2 stands at the forefront of innovation, leveraging the advanced capabilities of the nRF52840 microcontroller. This 32-bit Arm® Cortex®-M4 CPU, operating at an impressive 64 MHz, empowers developers for a wide range of projects. The added compatibility with MicroPython enhances the board's flexibility, making it accessible to a broader community of developers.
The standout feature of this development board is its Bluetooth® Low Energy (Bluetooth® LE) capability, enabling effortless communication with other Bluetooth® LE-enabled devices. This opens up a realm of possibilities for creators, allowing them to seamlessly share data and integrate their projects with a wide array of connected technologies.
Designed with versatility in mind, the Nano 33 BLE Rev2 is equipped with a built-in 9-axis Inertial Measurement Unit (IMU). This IMU is a game-changer, offering precise measurements of position, direction, and acceleration. Whether you're developing wearables or devices that demand real-time motion tracking, the onboard IMU ensures unparalleled accuracy and reliability.
In essence, the Nano 33 BLE Rev2 strikes the perfect balance between size and features, making it the ultimate choice for crafting wearable devices seamlessly connected to your smartphone. Whether you're a seasoned developer or a hobbyist embarking on a new adventure in connected technology, this development board opens up a world of possibilities for innovation and creativity. Elevate your projects with the power and flexibility of the Nano 33 BLE Rev2.
Tech specs
Microcontroller | nRF52840 (datasheet) | |
USB connector | Micro USB | |
Pins | Built-in LED Pin | 13 |
Digital I/O Pins | 14 | |
Analog Input Pins | 8 | |
PWM Pins | All digital pins (4 at once) | |
External interrupts | All digital pins | |
Connectivity | Bluetooth® | u-blox® NINA-B306 |
Sensors | IMU | BMI270 (3-axis accelerometer + 3-axis gyroscope) + BMM150 (3-axis Magnetometer) |
Communication | UART | RX/TX |
I2C | A4 (SDA), A5 (SCL) | |
SPI | D11 (COPI), D12 (CIPO), D13 (SCK). Use any GPIO for Chip Select (CS) | |
Power | I/O Voltage | 3.3 V |
Input Voltage (nominal) | 5-18 V | |
DC Current per I/O Pin | 10 mA | |
Clock Speed | Processor | nRF52840 64 MHz |
Memory | nRF52840 | 256 KB SRAM, 1 MB flash |
Dimensions | Weight | 5 gr |
Width | 18 mm | |
Length | 45 mm |
Resources for Safety and Products
Manufacturer Information
The production information includes the address and related details of the product manufacturer.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
https://www.arduino.cc/
Responsible Person in the EU
An EU-based economic operator who ensures the product's compliance with the required regulations.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
Phone: +39 0113157477
Email: support@arduino.cc
Documentation
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Get Inspired
An intelligent device to track moves with responses during an interactive space with mapping, backlight, music and smart sculptures. This project makes use of a machine learning algorithm capable of tracking and detecting moves to identify associated gesture recognition through a microcontroller. Smart sculptures, lighting, music and video projection to trigger with each assigned gesture, creating a powerful AV experience highlighting the incredible potential of TinyML for the performing arts. This allows the corresponding media set Tiny ML in interactive to play when the right move was made because all these elements interact to create a new experience. This allows us to create Interactive installations, these sculptures use a combination of motors, sensors, and other electronics to create an immersive and interactive experience for the viewer. They may include projections, sound, and other sensory elements to create a complete experience.
With an array of onboard sensors, Bluetooth® Low Energy connectivity, and the ability to perform edge AI tasks thanks to its nRF52840 SoC, the Arduino Nano 33 BLE Sense is a great choice for a wide variety of embedded applications. Further demonstrating this point, a group of students from the Introduction to Embedded Deep Learning course at Carnegie Mellon University have published the culmination of their studies through 10 excellent projects that each use the Tiny Machine Learning Kit and Edge Impulse ML platform. Wrist-based human activity recognition Traditional human activity tracking has relied on the use of smartwatches and phones to recognize certain exercises based on IMU data. However, few have achieved both continuous and low-power operation, which is why Omkar Savkur, Nicholas Toldalagi, and Kevin Xie explored training an embedded model on combined accelerometer and microphone data to distinguish between handwashing, brushing one’s teeth, and idling. Their project continuously runs inferencing on incoming data and then displays the action on both a screen and via two LEDs. Categorizing trash with sound In some circumstances, such as smart cities or home recycling, knowing what types of materials are being thrown away can provide a valuable datapoint for waste management systems. Students Jacky Wang and Gordonson Yan created their project, called SBTrashCat, to recognize trash types by the sounds they make when being thrown into a bin. Currently, the model can three different kinds, along with background noise and human voices to eliminate false positives. Distributed edge machine learning The abundance of Internet of Things (IoT) devices has meant an explosion of computational power and the amount of data needing to be processed before it can become useful. Because a single low-cost edge device does not possess enough power on its own for some tasks, Jong-Ik Park, Chad Taylor, and Anudeep Bolimera have designed a system where
FAQs
What is the difference between Rev1 and Rev2?
There has been some changes in the sensors, components and PCB design to improve the experience of the users:
- Replacement of IMU from LSM9DS1 (9-axis) for a combination of two IMUs (BMI270 - 6-axis IMU and BMM150 - 3-axis IMU).
- Replacement of power supply MPM3610 for MP2322.
- Addition of VUSB soldering jumper on the top side of the board.
- New test point for USB, SWDIO and SWCLK.
Can the Nano 33 BLE Rev2 be programmed using MicroPython?
Yes, the Nano 33 BLE Rev2 is compatible with MicroPython, to learn more visit the board installation in the Arduino and MicroPython documentation.
Do I need to change my sketch used in the previous revision?
For sketches reliant on the LSM9DS1 library to access IMU data, given the recent modification in this component, users are advised to transition to the Arduino_BMI270_BMM150 library, tailored for the updated combined IMU. For more information visit the Arduino Help Center.
Is it also compatible with the Arduino Nano Screw Terminal Adapter?
Yes, the Arduino Nano 33 BLE Rev2 is compatible with the Arduino Nano Screw Terminal Adapter.
Is it also compatible with the Arduino Nano Motor Carrier?
Yes, the Arduino Nano 33 BLE Rev2 is compatible with the Nano Motor Carrier.
Are the headers included in the box?
Yes, the headers are not soldered to the board but are included in the box.