You step into a fitting room carrying two jackets and a pair of trousers. You like each piece, but you are not sure they work together – and which jacket is better. Normally, you would take a photo, message a friend, or walk back outside to ask a store assistant. But imagine if you could simply look into the mirror and tap “Scan your look” instead.

A few seconds later, the mirror recognizes what you are wearing, identifies colors, and suggests how you might complete the outfit. Perhaps with a shirt or an accessory – available in the store – that will tie everything together.

This is the experience behind the Smart Mirror example running on VENTUNO Q. It begins as a personalized style advisor, but it also points towards a new kind of retail experience: one in which physical spaces can understand what customers are doing and offer relevant guidance in real-time.

We recently published a full tutorial on Arduino Docs that you can follow to build your own Smart Mirror with the board. While straightforward to replicate, we think this use case is very interesting both from a technological and an experiential standpoint. Let’s dive in.

A mirror that understands what it sees

The Smart Mirror application uses a USB camera to provide a live video feed. When you tap “Scan your look”, the latest camera frame is analyzed locally on VENTUNO Q by the Qwen3-VL Model, accessed through the Arduino VLM Brick.

The model is built to analyze the image, identify the most prominent garment and its color, and return two short sentences: a description of the outfit and a related styling suggestion. The result is then displayed directly over the live camera view.

The experience is designed to be intentionally simple: just look in the mirror, start the scan, and receive an immediate recommendation.

Behind that simple interaction, however, several components are working together:

• Continuous camera acquisition
• Local visual AI inference
• Prompt-controlled text generation
• A browser-based user interface
• Real-time communication between the frontend and the application

This combination is what makes the example useful as more than a technical demonstration. It shows how visual AI can become part of a complete user experience.

From demo to retail use case

With a Smart Mirror powered by VENTUNO Q, customers don’t need to go through racks or search through the store’s catalog. Once they find one piece they like, the mirror recognizes the garment and provides an immediate suggestion: “You’re wearing a navy jacket. Try pairing it with light-colored trousers for more contrast.” And that is only the beginning.

Connected to the retailer’s catalog and inventory system, the same application could recommend matching products that are currently available in the store. It could show alternative colors, suggest accessories, display available sizes, or generate a QR code that allows the customer to save the outfit on their phone.

In this scenario, the Smart Mirror is no longer just giving generic fashion advice. It becomes part of the retail experience and connects visual understanding with real business information – providing a concise and relevant response.

Why local AI matters: all privacy, no latency

One of the most important characteristics of the example is that the Vision Language Model is designed to run locally on the board. The camera image does not need to be sent to an external cloud service for inference: the image is processed on VENTUNO Q, and the generated answer is returned directly to the local browser interface.

This matters, especially in camera-based applications. By processing images locally, developers can design experiences in which visual data remains closer to where it is generated, potentially reducing the need to transmit image data to external services and helping protect user privacy. 

It also reduces dependency on network latency and external AI services. The application can remain responsive even when the internet connection is slow, unreliable, or unavailable.

Turning AI output into a product experience

In the Smart Mirror example, the prompt is designed to produce a very specific result. The model is asked to identify the main clothing item, detect its color, and provide a short styling suggestion. The application also randomizes the opening phrase and the start of the recommendation to provide variation across repeated scans. For this example, the prompt behavior can be customized through a single prompt.py configuration file.

This is a small detail, but it illustrates an important point. The model in this example is configured with a relatively low temperature and a short token limit, helping it produce concise and predictable responses. In a commercial retail application, the prompt could be expanded with product rules, brand guidelines, seasonal collections, availability data, or customer preferences.

How the application works

The technical architecture is designed to be streamlned. The camera continuously captures frames, and the latest frame is stored in a shared buffer. The browser displays the live feed through an MJPEG stream exposed by the backend.

The flow could look like this:

Camera image > garment recognition > product matching > personalized recommendation > customer action

The generated result is then sent back to the browser and displayed in the mirror overlay.

In the code, the application uses two App Lab Bricks: the Vision Language Model Brick and the Web Interface Brick.

The camera could be replaced with another image source. The prompt could be adapted to a completely different domain. The web interface could be redesigned for a kiosk, a touchscreen, or an embedded display.

Beyond fashion

The most valuable lesson from the Smart Mirror example is that the architecture is not limited to clothing: the same model can be applied in several domains.

• Assisted dressing: The system could help users identify colors, distinguish garments, or check whether items match. For people with visual impairments, it could describe the clothing they are currently wearing. For the elderly or users who need cognitive support, it could provide simple guidance when choosing an outfit.
• Beauty and personal care: A similar mirror could support makeup tutorials, eyewear selection, hair styling, or skincare routines. The camera would analyze the visible situation, while the prompt and application logic would determine what type of recommendation is appropriate.
• Hospitality: A hotel-room mirror could provide recommendations based on guests’ clothing, the weather, the planned activity, or the dress code of a venue. For example, it could suggest bringing a jacket before an evening event or recommend more comfortable footwear for a walking tour.
• Industrial operator assistance: The same architecture can also move from consumer applications to professional environments. Instead of recognizing a shirt or jacket, the model could identify tools, machine components, labels, or personal protective equipment. An operator could stand in front of a workstation and receive a short visual instruction: “You are not wearing safety goggles. Put them on to protect your eyes before starting the machine.”

Such systems would require careful validation and should not replace certified safety mechanisms. But the Smart Mirror example shows the underlying technical pattern clearly: a camera observes the situation, a model interprets it, and the application provides immediate guidance.

A reusable pattern for Physical AI

Advanced users like Kamitronix have already been experimenting with Smart Mirrors built with the Arduino^® UNO Q board, providing real-time feedback and information as you check out your look before going out. But while these may appear to be mainly user-interface applications, they also point toward a broader Physical AI workflow. Instead of a text on a screen, the final response could activate a light, control a motor, change a machine setting, notify an operator, or trigger another application.

That is why examples like the Smart Mirror are useful. They make advanced AI capabilities approachable, but they also reveal a reusable architecture for building systems that interact with the real world.

We started with a simple question: What if a mirror could understand what you are wearing? Mapped to a real-world use case, it can become an AI-assisted fitting room that recommends matching products, connects customers with inventory, and improves the in-store journey. More broadly, the same architecture can support assistive technology, hospitality, personal care, and industrial operator guidance.

The most interesting part is the pattern it demonstrates: using local visual AI to transform a passive object into an interface that can understand its context, respond in real-time, and improve people’s experience.

Ready to kickstart your journey in scalable smart interfaces and Physical AI? Get your VENTUNO Q from the Arduino Store today. 

VENTUNO Q is also available through our official distribution partners: DigiKey, Farnell, Kubii, Mouser, Robu.in, and RS, along with our other authorized distributors and resellers.   

Arduino, UNO, VENTUNO, and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

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Luca Di Lorenzo (@LucaDilo on YouTube) recently worked with us on what was meant to be a simple robot assistant, but soon became something much more fun: a tiny, expressive desktop companion that notices you, listens to you, and talks back. Based on the Arduino^® UNO Q board, Nuvi is designed to run everything it needs on-device, no internet required for the core experience. 

We think it’s the cutest, but maybe our team is partial to the project because the whole Arduino office in Turin, Italy loves its real-life inspiration: Kaito, the Shiba Inu our colleague Assunto sometimes brings to work.

Di Lorenzo says Nuvi’s first design worked but felt like a machine. With Kaito as a reference, he was able to find the inspiration to build something we would all actually want on our desk.

Talk to it and control your smart home

Nuvi is under 30 cm tall (approximately 12 inches) and packs a surprising amount into that compact form. It is built to wake up when you approach, responds when called by name, and holds a conversation powered by Gemini – which handles both understanding speech and generating replies in a single step, with no separate speech-to-text layer. For speaking without the cloud, Nuvi uses Piper, a voice engine running entirely on the board, so responses feel immediate. It can search the web when needed, toggle smart home devices by voice or a hand gesture, and read out room temperature and humidity when you flash it an open palm.

Beyond conversation, Nuvi has animated eyes that express different moods, moving ears and arms, head pan and tilt, touch reactions on its head and belly, and — just for fun — a dance.

How UNO Q makes this possible

Nuvi runs on UNO Q, and the board’s dual-brain architecture is exactly what a project like this one needed. The real-time microcontroller side is designed to handle all the physical control: four servos for the arms and ears, two serial servos for the neck, the display, sensors, and touch inputs. Meanwhile, the Linux side is built to run the AI pipeline, the voice engine, and the application logic. Everything lives on the board itself, not on a connected PC. Two jobs, one board, no compromises.

Build your own desk companion!

Di Lorenzo shares everything you need right here on Arduino Project Hub: 3D files, electronics, code, and a full step-by-step tutorial. If you’ve been looking for a project that shows just how much personality you can pack into a UNO Q build, this is it. Of course, feel free to customize it to resemble your favorite pet!

UNO Q is available on the Arduino Store, and can be ordered from DigiKey, Farnell, Mouser, Newark, RS Components, Robu.in, and other authorized distributors and resellers worldwide.

Arduino,UNO, and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

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Are you ready for Maker Faire Bay Area? We sure are! The family-friendly festival of invention, creativity, and hands-on DIY culture – some call it the “the greatest show-and-tell on Earth” – is holding its special 20^th anniversary edition this year, and we wouldn’t dare miss the celebrations. 

Join us on September 25th–27th in Vallejo, California with thousands of makers, educators, and tech enthusiasts from all over the world: at Maker Faire, everyone brings their own ideas and leaves with loads of inspiration.

Find us at the Make: magazine booth

Stop by the Arduino Space inside the Make: magazine booth to hang out with the team, check out our latest hardware, and see what’s next for open-source electronics:

• Get hands-on with Arduino® Plug and Make Kit, our tried-and-true beginner kit designed to make prototyping easier than ever.
• Have fun with Arduino® UNO Q: we’re bringing the most playful live demos to Maker Faire Bay Area – including a custom handheld retro gaming console built around our versatile dual-brain board.
• Take a first look at Arduino® VENTUNO Q board, our newest launch, designed to push Physical AI to the next level.

Plus, we’ll be offering a special discount code for Faire visitors!

Don’t miss Massimo Banzi on the Foundry Stage

Mark your calendars for Sunday, September 27th at 1PM PDT! Arduino co-founder Massimo Banzi will be taking the Foundry Stage for a special talk showcasing “One year of projects with the UNO Q.” We knew it was something special when it launched in October 2025, but the range of ideas you all have tested and brought to life in the past 12 months has been nothing short of incredible. Massimo’s talk will feature some of the most amusing, surprising, and advanced community-built ideas we’ve seen – and we know it will only inspire you to go further! 

Get your tickets and find all the event’s info on the official website. We look forward to seeing you at Maker Faire Bay Area 2026.

Arduino, Plug and Make, UNO, and VENTUNO are trademarks or registered trademarks of Arduino S.r.l.

The post Bring ideas, leave inspired: Maker Faire Bay Area 2026 is coming! appeared first on Arduino Blog.

Expense calculations in the industrial world tend to be unintuitive to individuals, because service and maintenance often consume larger chunks of the budget than the equipment itself. For that reason, modifications to equipment are usually seen as too risky to justify — they can too easily impact serviceability. So, what do you do when you want to bring an old piece of equipment into the modern age? Michael Bryan Ross’ solution was to use AI to look at analog gauges.

Ross wanted to address a simple and common problem: the equipment has an analog gauge and it would be nice to have that value available in digital form for monitoring and logging.

Most of us, when presented with that problem, would take the easy and seemingly reasonable approach. That might be something like replacing the analog gauge with a microcontroller outfitted with an ADC (analog-to-digital converter).

But very few plant managers or manufacturing engineers are going to give the green light on a modification like that. Not only is there upfront downtime to consider, but it also puts the equipment and future serviceability at risk.

Ross’ solution, on the other hand, is much easier to approve. That’s because it doesn’t require any modification to the equipment at all. In fact, it doesn’t even need to make physical contact with the equipment.

It works by using a camera and AI running on the edge to read the analog gauge. In this case, “the edge” is an inexpensive Arduino® UNO Q and it looks at the gauge through a standard USB webcam. The UNO Q runs a MobileNetV3-Small model through ONNX Runtime.

To test that — and to gather the images needed to train the model in the first place — Ross built a physical device with a real analog gauge driven by an actual pressure sensor. To create a training data set, Ross simply collected a bunch of images of the gauge’s needle in different position, then had GPT-5.6 read the black ticks to classify them by numeric value.

Ross acknowledges that the resulting model isn’t perfect. In particular, it tends to lose accuracy at very high and very low ends of the gauge range. But that is a fixable problem (largely through training). The concept holds: that this approach makes it possible digitize analog gauges, without spending much money and without modifying equipment.

The post Digitize analog gauge readings with edge AI appeared first on Arduino Blog.

Go on your favorite online shopping platform, and you’ll find any number of smart doorbell options. Click to purchase, have it delivered, install it, download some app. But where’s the fun in that? And also, don’t you wonder how that thing works? 

That thing that watches you and your loved ones go in and out, learning to recognize friends and delivery people, helping you check on your home even while you are gone. If you are thinking about the security of the place where you live, why not be more hands-on about your privacy while you’re at it? 

Of course, we have an easy (even fun!) solution to these concerns: just build your own smart doorbell, and have complete control – not only over who comes and goes, but also over where your data is stored and how it’s handled. 

Massimo Banzi and Andrea Richetta are going to show you how to build your own smart doorbell, based on a computer vision model that can run locally on the Arduino^® UNO Q board. 

Just click the ‘notify me’ button and follow the live build, on September 22nd at 3PM CET / 9AM ET. In one hour, we’ll see the whole project come together – and you’ll have a chance to ask questions directly to the Arduino team. 

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The heart of the entire cyberdeck movement is personalization. Instead of shopping within the limits of what manufacturers deem to be marketable, cyberdeck builders can create designs that reflect their own personal tastes. But even so, most cyberdecks fit within a fairly narrow aesthetic—usually some variation of cyberpunk or cassette futurism, fitting the original Gibson source material. Natasha Dzurny (AKA TechnoChic) took things in a completely unique direction by building her Arduino® UNO Q cyberdeck as an Avenue Q-style puppet.

Avenue Q is musical that originally appeared off-Broadway way back in 2003 and has since enjoyed stints on Broadway, the West End, and Las Vegas. It is, essentially, a humorous adult spin on Sesame Street, which means it has all kinds of Hensonian puppets. Dzurny’s interest in Avenue Q and its phonetic similarity to “Arduino Q” led to this creation.

The electronic components are standard fare for a cyberdeck and include the UNO Q (2GB), a mini Bluetooth keyboard/touchpad device, a portable USB battery pack, and a 5” HDMI display. There is also a mini USB webcam hidden away for recorded puppet shows.

The real magic and creativity went into the “enclosure,” which is really the body of the puppet. To craft that, Dzurny started with a spherical mold made of translucent plastic. She then cut that to shape and built a frame structure inside, onto which she could mount the components. Once covered in thick fur, the sphere became a puppet head that Dzurny could put her hand into, so she could open and close the mouth. A couple of big expressive eyes completed the look and a chain strap made the puppet/cyberdeck easy to carry.

Dzurny’s project is the perfect example of why people love cyberdecks: because they’re a perfect outlet for creative expression that reflects the builder’s own personality. 

The post This cyberdeck is a… puppet? appeared first on Arduino Blog.

Every day, hundreds of thousands of kits are prepared in warehouses before components ever reach an automotive production line. While robots have become commonplace in modern manufacturing, many upstream logistics activities still rely heavily on human operators performing repetitive pick-and-place and kitting tasks.

What if robots could learn these operations the same way humans do: by simply watching a demonstration?

That question was at the heart of I-GENIUS, a research project coordinated by MYWAI within the European ARISE initiative with Centro Ricerche FIAT (CRF) and the University of Genoa’s Department of Mechanical, Energy, Management and Transportation Engineering (DIME).

The project explored new approaches to Human-Robot Interaction, combining AI, computer vision, and robotics to enable machines to acquire manipulation skills from minimal human guidance.

One of the project’s key outcomes was VILMA (Visual Imitation Learning for Manipulation Activities), an AI-powered toolkit integrated into MYWAI’s EDGE AI middleware platform. VILMA helps enable robots and humanoids to learn complex manipulation tasks from one-shot human demonstrations, aiming to significantly reduce programming effort while improving flexibility in dynamic industrial environments.

The technology was evaluated in a large-scale automotive warehouse use case developed together with CRF and reproduced within the robotics laboratories at DIME.

Today, MYWAI is bringing this technology to a broader community of developers, makers, and robotics innovators by porting the VILMA Toolkit to new Arduino products powered by Qualcomm Dragonwing processors, including both the Arduino^® UNO Q and VENTUNO Q boards.

This demonstration showcases the potential for edge-native robotics applications that use imitation learning techniques on hardware platforms built to support compact form factors and efficient power consumption.

At the iGenius final presentation, the founder and CEO of MYWAI, Fabrizio Cardinali, stated: “The dual-brain architecture of the UNO Q and VENTUNO Q platforms is an ideal foundation for MYWAI’s next generation of Edge AI robotics. After validating distributed intelligence concepts within the ARISE I-GENIUS project, we are now leveraging these platforms to bring World Action Models closer to the edge through the latest release of the MYWAI EdgeAI Management Platform and its mobile tracker, HEDGELOG. By combining One-Shot Video Imitation Learning with edge-native AI execution, we aim to enable robots and intelligent industrial machines to acquire, distribute, adapt, and execute complex manipulation skills with unprecedented flexibility and scalability.”

Watch the full demonstration of the I-GENIUS project and see VILMA in action in this video.

The MYWAI VILMA agent

VILMA is a visual imitation learning toolkit that helps enable robots to learn manipulation tasks from human demonstrations. It is designed to process RGB-D recordings or MP4 videos to extract hand and object trajectories, generate reusable robot skills using Dynamic Movement Primitives (DMPs), and produce robot-ready trajectories for playback. It is constructed to serve as the demonstration learning module, supporting rapid robot programming, skill reuse, and deployment.

The AI pipeline

One-shot demonstration acquisition

The one-shot demonstration acquisition step is set to record a human performing the task or retrieve an existing demonstration from a selected MYWAI equipment event. It stages the video, RGB frames, depth data, and camera parameters, and allows the user to select the target object for tracking. This information provides the inputs required by the remaining pipeline stages. 

Hand detection

Using MediaPipe, this stage is structured to detect 21 hand landmarks in each RGB frame and combine their 2D positions with depth data to calculate 3D camera coordinates. For demonstrations loaded from MYWAI, the staged RGB and depth data are processed through the same pipeline. Kalman smoothing and previous-position retention improve tracking robustness, and the resulting trajectories can be saved back to the MYWAI event. 

Object detection

Using a YOLO model, this stage is designed to detect or track the object selected through the local or MYWAI interface. It combines the bounding-box centre with depth information to calculate the object’s 3D position, applies Kalman smoothing, and saves the trajectory and annotated frames. These results can then be included in the pipeline artifacts stored in MYWAI. 

Trajectory and segmentation

This stage is constructed to load the smoothed hand and object trajectories, estimate the grasp point from the hand’s proximity to the object, and detect the release point from the object’s movement and stabilization. It uses the hand trajectory as the main motion path and divides it into reach, grasp, move, release, and post-release phases. The trajectories, event indices, and segmentation metadata can be packaged as MYWAI event data, a time and space data fusion format developed by MYWAI for its AI-IoT management platform particularly geared towards Multimodal AI and, next, towards World Action Models. 

DMP generation

The DMP-generation stage is designed to learn separate Dynamic Movement Primitive models for the reach and move phases. It evaluates different regularization values, selects the model that provides the best accuracy and smoothness, validates the reproduced motion, and saves the trained models and trajectories. These DMP artifacts can be uploaded to MYWAI with the other pipeline results for later retrieval and reuse. 

Demonstration

The demonstration stage is structured to convert the generated DMP trajectory into Cartesian robot positions using the configured scale, offset, and rotation, then apply inverse kinematics to calculate the joint trajectory. The robot model may be loaded from the selected MYWAI equipment, and the resulting motion is displayed through the MYWAI 3D Viewer, synchronized with the recorded video and its grasp and release events. 

DMP adaptation with new goal and new object

The adaptation stage is set to load the learned skill – either from the current pipeline or a restored MYWAI event – and detect a new target object using RGB and depth data. It calculates the 3D offset between the original and new objects, redirects the reach and move trajectories toward the new pick and release positions, and preserves the demonstrated motion characteristics. The adapted trajectory can then be visualized with the MYWAI 3D Viewer or sent to the robot. 

Live streaming adaptation and UNO Q and VENTUNO Q support

The Live stream phase represents the deployment and real-time inference stage of the VILMA Agent. While the initial learning phase is conducted on the MYWAI platform to generate Dynamic Movement Primitives (DMP), the Live stream phase focuses on shipping these DMPs along with a fine-tuned YOLOv8 model, supported today on UNO Q and VENTUNO Q.

Architecture and components

As illustrated in the system schematic below, the architecture is designed as a distributed setup divided into an Edge AI Layer for intelligence and a Communication Layer for hardware interfacing.

Let’s break down how the live stream pipeline is working considering the VENTUNO Q version.

1. Edge AI layer (VENTUNO Q)

Running on VENTUNO Q, this layer is structured to handle high-level decision-making.

• VILMA Control Loop: The primary application logic responsible for the overall control loop. It It is designed to orchestrate object detection and performs DMP Adaptation to translate learned human motions into the current physical environment.
• Video Object Detection Brick: This component runs on VENTUNO Q to manage the inference flow. It receives the incoming video feed and communicates with the inference service.
• Docker: YOLOv8 Inference Service is formed as a containerized service that runs the quantized YOLOv8 model. This model is designed to be fine-tuned and deployed via the Edge Impulse platform using the “Bring Your Own Model” feature.

2. ROS 2 communication layer (Workstation)

A separate workstation connected directly to the devices manages the high-bandwidth data streams and robotic control via ROS 2.

• ROS 2 Streaming Node: Interfaces with the ZED Camera/Depth Sensor to capture raw visual data, publishing it as a /camera_feed to VENTUNO Q.
• ROS 2 Command Node: This node acts as a wrapper around the Fairino Python SDK. It is engineered to serve as the receiver for the /learned_trajectory sent from the edge device, utilizing the SDK to directly control the robot and help ensure it accurately follows the planned trajectory.

3. Physical hardware

External hardware is connected to complete the runtime VILMA ecosystem, namely:

• ZED Camera: The stereo camera which is engineered to capture the image and depth data required for the vision system.
• Fairino FR10 Robot: The robotic arm that is constructed to execute the pick-and-place tasks based on the trajectories computed by VILMA.

Component communication and data flow

The communication between these components is designed for low-latency execution as shown in the schema above:

• Vision Input: The Workstation is engineered to stream the /camera_feed (image and depth) to VENTUNO Q.
• Edge Inference: The VILMA Control Loop is designed to utilize a WebSocket stream to send frames to the Docker YOLOv8 Inference Service. The service returns the detected object bounding box to the control loop.
• Motion Adaptation: The system is structured to take the detected object positions and adapts the human-learned DMP to calculate a precise pick-and-place trajectory.
• Robotic Execution: The resulting /learned_trajectory is published back to the Workstation’s ROS 2 Command Node, which drives the Fairino FR10 robot to complete the task.

This modular approach is designed to allow the heavy vision processing and motion adaptation to happen on the edge (VENTUNO Q) while leveraging the robust ROS 2 ecosystem for robot communication and sensor streaming.

To learn more about the project and MYWAI’s EDGEAI Platform and Middleware, visit myw.ai.

Qualcomm branded products are products of Qualcomm Technologies, Inc. and/or its subsidiaries. 

Arduino, UNO, and VENTUNO are trademarks or registered trademarks of Arduino S.r.l.

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A lot of newer 3D printers incorporate sophisticated sensor suites and cameras to detect problems with print jobs, like the dreaded “spaghetti failure.” Those work pretty well and prevent wasted time, wasted filament, and even damage to the printer. But what if you don’t have a printer with those features? Or if you want to protect your privacy? Then you can follow Philipp Schweizer’s guide to use an Arduino UNO Q as your local 3D printing watchdog.

Schweizer’s approach is to use the UNO Q and a camera to detect anomalies, rather than specific problems. It doesn’t look for spaghetti failure or clogging. Instead, it looks for a normal print. Anything abnormal gets flagged. That dramatically reduces the amount of training data required and accounts for all visible issues, whether they were anticipated and trained for or not.

The hardware required for this includes an UNO Q (4GB model), an Arduino^®UNO Media Carrier, an V2-style IMX219 camera module, and a custom-printed mount with fasteners. The goal with that mount is to point the camera at the hot end and below, so it will likely require customization to fit the 3D printer model in question.

All of the magic happens thanks to the FOMO-AD anomaly model from Edge Impulse, implemented through Arduino App Lab. That requires training on images of normal prints and Schweizer explains how it is easy to collect those automatically during print jobs. By keeping the image resolution low (632×480), the overhead remains minimal and the model efficient.

As Schweizer points out, an overeager watchdog is worse than not having a watchdog at all. For that reason, Schweizer built his app so that it only automatically pauses a print if at least three out of the last four images show an anomaly. When that is the case, it pauses the job through the Moonraker API (standard for Klipper-based systems). For those who don’t use Klipper, it is possible to integrate the watchdog into Octoprint or other control software. It also sends an MQTT message compatible with Home Assistant for notifications and hosts a webpage where the status is visible.

All of that works without leaving the local network, so it is great for those who value privacy and security. Even if those factors don’t concern you, this is an affordable watchdog solution that doesn’t require a specific 3D printer model or a subscription service.

The post Turn Arduino® UNO™ Q into your local 3D printing watchdog appeared first on Arduino Blog.

Physical AI is based on the idea that intelligence shouldn’t stop at perception: instead, it should bring to life systems able to sense their environment, reason about it, and act on it – all in one continuous loop. It’s what makes the difference between a device that observes and one that acts.

Autonomous mobile robots are one of the clearest expressions of that idea in the real world. Imagine an autonomous delivery robot working around a warehouse, carrying items to the correct shelf or placing them on a conveyor belt for inspection or packaging. On its way, it may have to stop as someone steps into its path, or adjust its route if a cart has been moved. It must understand its surroundings, determine its position, calculate a path, and respond as the environment changes, until it reaches the right destination. 

It must control its wheels precisely, read sensors continuously, and react quickly to nearby obstacles – all at the same time.

In all autonomous mobile robots, or AMRs, these tasks require two different kinds of computing: navigation, computer vision, and AI benefit from the flexibility and performance of Linux; motor control and time-sensitive sensor management require the responsiveness of a microcontroller.

VENTUNO Q brings these two worlds together, combining a Linux-capable MPU with a real-time MCU on a single platform. To explore the potential of this architecture, let’s imagine how it could become the central computing platform for a food-delivery AMR, where perception, ROS 2, Edge AI and physical control could work together.

Two computing worlds, one robot

In a conventional AMR prototype, high-level computing and motor control are often distributed across separate boards. A Linux computer runs ROS 2 and processes data from cameras and LiDAR, while a microcontroller manages motors, encoders and local sensors. This approach works, but it also creates integration work. Developers must connect the boards, define their communication protocol, maintain multiple development environments and debug the interaction between systems designed independently from one another.

VENTUNO Q provides a more integrated starting point. Its MPU can host Linux, ROS 2, navigation software, computer vision and AI workloads. Its MCU can remain close to the physical robot, managing wheel control, encoder feedback, inertial measurements, short-range sensing and communication with motor drivers. The separation of responsibilities is clear: the MPU determines where the robot should go, while the MCU turns that decision into controlled physical movement. Together, the two sides allow the system to sense, decide, and act.

Sense: giving the robot an understanding of its environment

As the delivery robot travels through a warehouse, hotel, or even a restaurant or public space where it is at work to deliver orders, it must continuously build a picture of what is happening around it. No single sensor can provide all the information it needs, so the architecture combines several complementary sources.

A 2D LiDAR can provide the geometric foundation for navigation. Its laser scans describe walls, corridors, furniture and other structures around the robot. Combined with wheel odometry and inertial data, these scans can be used by SLAM software to build a map and later help the robot estimate its position within it. However, a LiDAR scanning on a single plane cannot describe every obstacle. A table surface, an open drawer or an object protruding into the robot’s path may sit above or below that plane.

A stereo-depth camera can add three-dimensional information, helping the robot perceive shapes and obstacles that may not appear clearly in a planar scan. A MIPI camera can provide visual input for more application-specific tasks, such as detecting people, recognizing a delivery station or checking whether a tray is present. 

^{Image shown for illustration purposes only}

Closer to the robot, an Arduino^® Modulino Distance node monitors blind spots or detects objects immediately in front of the chassis. A Modulino Movement provides acceleration and angular-rate data, contributing to motion estimation and helping identify unusual vibration, collisions or unexpected movement.

Wheel encoders complete this picture by measuring how the robot is actually moving. The MCU can use their feedback to regulate wheel speed, while the resulting odometry can be shared with the navigation software running on Linux. Rather than relying on a single “smart” sensor, the robot develops awareness by combining geometric, visual, inertial and mechanical information.

Decide: ROS 2 as the software backbone

On the MPU, ROS 2 provides a common software environment for connecting all these elements. The LiDAR publishes laser scans. The camera pipeline provides images or depth information. The robot publishes odometry and inertial measurements. Navigation components consume this data to estimate the robot’s position, plan a route and respond when the environment changes.

^{Image shown for illustration purposes only}

During its first exploration of a space, a ROS 2 SLAM package can combine LiDAR data and odometry to create a map. Once that map is available, the robot can localize itself and use Nav2 to move between destinations.

If the application sends the robot from the warehouse to the packaging station, Nav2 calculates an appropriate route and continually updates it as new sensor information arrives. It then generates velocity commands describing how quickly the robot should move forward and rotate. Those commands express the intended movement, but they do not directly control the motors. That responsibility moves from the Linux domain to the MCU.

Act: from a ROS 2 command to wheel movement

When Nav2 publishes a velocity command, an MPU-to-MCU communication layer transfers the request to the real-time control application. The MCU translates the requested linear and angular movement into targets for the left and right wheels. It then communicates with the motor controllers – potentially over CAN – and uses encoder feedback to maintain the required wheel speed.

This control loop can run with predictable timing, independently of the larger ROS 2 software graph operating on Linux. Information also travels in the opposite direction. Encoder measurements, inertial data, short-range distance readings and drive-system diagnostics can move from the MCU to the MPU, where they become available to the ROS 2 application.

This connection between the two computing domains is one of the most important elements of the architecture. It allows high-level navigation software to interact with the physical robot without asking Linux to manage every time-sensitive electrical and mechanical detail.

It also creates space for local reactions. If a close-range sensor detects an object immediately in front of the robot, the MCU can place the drive system into a controlled stop state and report the event to the MPU. The navigation layer can then reassess the situation, update the route or notify the application. The result is a robot in which intelligence and responsiveness complement one another.

Adding context with edge AI

LiDAR, odometry and Nav2 can already allow the robot to navigate through a mapped environment. AI becomes valuable when the robot needs to understand the meaning of what it perceives. The navigation system may detect an obstacle, for example, but a vision model could determine whether that obstacle is a person, a chair or a delivery cart. This additional context enables more appropriate behavior.

When a person approaches, the robot could slow down, wait or announce that a delivery is arriving. At its destination, a visual model might recognize the delivery zone or check whether someone has removed the tray. The same platform could also inspect the route for objects left on the floor or use vibration data to identify abnormal mechanical behavior.

Edge Impulse offers one possible workflow for developing this kind of application-specific intelligence. Through its ROS 2 integration, an inference model can consume image data and make its results available to other parts of the robotic application. ROS 2 helps the robot decide how to navigate. Edge AI helps it interpret the context in which that navigation takes place. And with the integrated Edge Impulse platform, you can choose to deploy directly into Arduino^® App Lab – it just takes one click.

Growing the prototype one capability at a time

Although the reference architecture includes navigation, depth sensing and AI, a developer would not need to introduce every capability at once. The first prototype could focus only on the mobile base, with the MCU controlling the motors and reading the wheel encoders. Once basic movement is reliable, the control layer could be connected to ROS 2 on the MPU so the robot can accept standard velocity commands and return odometry data.

Adding LiDAR would introduce mapping, localization and autonomous navigation. Depth sensing and short-range sensors could then improve obstacle awareness. Finally, visual AI could transform the generic mobile platform into a delivery robot capable of recognizing people, destinations and application-specific events. This progressive approach keeps each development step understandable while preserving a clear path toward a more capable system.

VENTUNO Q does not remove the engineering challenges involved in building an AMR. Mechanical design, power management, motor sizing, navigation tuning, sensor placement and safety still require careful work. Its value is in providing a more coherent place to begin.

From perception to motion

A food-delivery robot is only one expression of this architecture. The same computing model could support warehouse vehicles, inspection robots, hospital logistics systems, agricultural platforms, and other autonomous machines.

The sensors, drive systems and AI models may change, but the fundamental challenge remains the same: connecting high-level intelligence with responsive physical action. By combining a Linux-capable MPU and a real-time MCU, VENTUNO Q gives developers a platform for exploring that complete chain. ROS 2, navigation, perception and edge AI can operate on the MPU, while the MCU manages the sensors and actuators that connect software decisions to the real world.

From understanding the environment to controlling the wheels, VENTUNO Q offers a unified starting point for prototyping the next generation of intelligent mobile robots.

Ready to kickstart your journey in robotics? Get your VENTUNO Q from the Arduino Store today. 

VENTUNO Q is available through our official distribution partners: DigiKey, Farnell, Mouser, Robu.in, and RS, along with our other authorized distributors and resellers.

Arduino, Modulino, VENTUNO, and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

The post Building smarter AMRs with the Arduino® VENTUNO™ Q board appeared first on Arduino Blog.

Robotic arms are extremely versatile, which is the entire point. But that versatility comes at the cost of complexity when it comes to programming and control. A few years ago, Kelton Serra from the Build Some Stuff YouTube channel simplified the situation by giving his custom robotic arm a puppet-style controller. Now he’s back with a redesigned version of that arm that incorporates many improvements.

Puppet control seems to be gaining popularity, because it is very intuitive. Instead of programming movements and actions, or even directing those with a conventional controller, puppet control works with a special input device that physically mimics the robot. Move a joint on the input device and the robot will move in exactly the same way.

The original version of Serra’s robotic arm had that kind of control input via a scaled-down replica of the arm with potentiometers instead of motors. The new version does, too. However, Serra put years of experience to use when he redesigned the arm, making it more affordable, easier to build, and better performing.

The brain of the robotic arm is an Arduino Nano ESP32 board. It monitors the potentiometers for input, then moves servo motors on the robotic arm to match. The analog voltage output from each potentiometer corresponds to a joint angle. The Arduino controls those servos through a servo driver board.

Most of Serra’s work went into mechanical design and CAD. He put a lot of effort into designing all of the parts to be easily 3D-printable and to make assembly straightforward. He also upgraded the mechanics, including with a belt drive for the wrist joint and a rack-and-pinion for the gripper.

This is a very accessible design that you can construct yourself with a 3D printer and an inexpensive set of components. To do that, head over to the Printables page to grab the files. 

The post Redesigning a popular robotic arm appeared first on Arduino Blog.