3D Printed Robot Wants To Be Your Pet

September 21, 2023 by Richard Baguley 1 Comment

Robots are cool. Robots you build yourself are cooler, especially ones that use stuff you have lying around already. Snoopy is a new open-source robot that uses an Arduino as a brain but with a 3D printed body and a short list of parts that can probably be sourced from the junk drawer. It’s still being developed, but it looks like a cool project heading in the right direction to produce an interesting robot.

It’s based on a new robot software platform called Kaia.ai that is built on top of the Robot Operating System 2 (ROS2), but with a more friendly and beginner-focused interface. Currently, the Snoopy project includes enough to get up and running with a printed frame and the electronics to install an Arduino running ROS2 that controls it. That’s an excellent place to start if you want to get into robotics, but without diving straight into the technical challenges of working with real-time operating systems.

It is also interesting that the previous project from the creator (called Kiddo) fell into the complexity trap, where you keep adding features and create an overly complex design that is a pain to build. Hopefully the designers have learned from Kiddo and will keep Snoopy simple.

We’ve covered plenty of other robot projects here at Hackaday, from ones that venture into nuclear reactors to ones that write your thank-you notes for you or give you hugs. We’ve even looked at how to give your robots a personality. Combine all those together with Snoopy and you could build a hugging, compassionate robot that has nice handwriting and can repair a nuclear reactor. And if you do, write it up and send it to our tips line!

Sand Drawing Plotter Runs On ESP32

July 21, 2023 by Lewin Day 3 Comments

Humans have always drawn lines in the sand, whether it’s to communicate a plan of attack or to indicate metaphorically a very real boundary. It’s also something we do just for the aesthetic pleasure, and this plotter from [aidenvigue] is great at performing in just that role.

The plotter traces patterns in the circular sand tray by dragging a small marble with a magnet. This is achieved with a pair of NEMA 17 stepper motors, set up in a polar coordinate fashion. One stepper motor controls the angle, while another motor controls the marble’s distance from the center point of the circle. It’s a simple way to build a circular plotter, and works far better than a Cartesian setup would for this geometry. The build uses an ESP32 as the brains of the operation. It hosts a web interface that allows various patterns to be selected and run on the device. It also runs a set of addressable SK6812 LEDs that light the sand rather nicely.

We’ve seen some great sand plotters before, and have always been particular fans of the larger variety. Video after the break.

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Hackaday Podcast 223: Smoking Smart Meter, 489 Megapixels, And Unshredding Documents

June 16, 2023 by Dan Maloney 10 Comments

Elliot’s back from vacation, and Dan stepped into the virtual podcast studio with him to uncover all the hacks he missed while hiking in Italy. There was a lot to miss, what with a smart meter getting snuffed by a Flipper Zero — or was it? How about a half-gigapixel camera built out of an old scanner, or a sonar-aimed turret gun? We also looked at a couple of projects that did things the hard way, like a TV test pattern generator that was clearly a labor of love, and an all-transistor HP frequency counter. More plastic welding? Hey, a fix is a fix! Plus, we’ll dive into why all those Alexas are just gathering dust, and look at the really, REALLY hard problems involved in restoring shredded documents.

Check out the links below if you want to follow along, and as always, tell us what you think about this episode in the comments!

Download a long series of ones and zeroes that, when appropriately interpreted, sound like two people talking about nerdy stuff!

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Hackaday Prize 2023: A DIY Voice-Control Module

May 15, 2023 by Lewin Day 13 Comments

If science fiction taught us anything, it’s that voice control was going to be the human-machine interface of the future. [Dennis] has now whipped up a tutorial that lets you add a voice control module to any of your own projects.

The voice control module uses a Raspberry Pi 4 as the brains of the operation, paired with a Seeed Studio ReSpeaker 4-microphone array. The Pi provides a good amount of processing power to crunch through the audio, while the mic array captures high-quality audio from any direction, which is key to reliable performance. Rhasspy is used as the software element, which is responsible for processing audio in a variety of languages to determine what the user is asking for. Based on the voice commands received, Rhasspy can then run just about anything you could possibly require, from sending MQTT smart home commands to running external programs.

If you’ve always dreamed of whipping up your own version of Jarvis from Iron Man, or you just want a non-cloud solution to turn your lights on and off, [Dennis’s] tutorial is a great place to start. Video after the break.

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An electronic neuron implemented on a purple neuron-shaped PCB

Hackaday Prize 2023: Explore The Basics Of Neuroscience With This Electronic Neuron

May 1, 2023 by Robin Kearey 2 Comments

Brains are the most complex systems in the universe, but their basic building blocks are surprisingly simple — the complexity arises from billions of neurons, axons and synapses working together. Simulating an entire brain therefore requires vast computing resources, but if it’s just a few cells you’re interested in, you don’t need much: a handful of op-amps and transistors will do the job, as [Sebastian Billaudelle] has demonstrated. He has designed an electronic neuron called Lu.i that does everything a real neuron does, in a convenient package suitable for educational use.

[Sebastian]’s neuron implements what’s known as the leaky integrate-and-fire model, first proposed by [Louis Lapicque] as a simple model for a neuron’s behavior. Basically, the neuron acts as an integrator that stores all incoming charge in a capacitor and generates a spiky output signal once its voltage reaches a certain threshold level. The capacitor is slowly discharged however, which means the neuron will only “fire” when it gets a strong enough input signal.

A couple of MCP6004 op-amps implement this model, with an LM339 comparator acting as the threshold detector. The neuron’s inputs are generated by electronic synapses made from logic-level MOSFETS. These circuits route signals between different neurons and can be manually set to either source or sink current, thereby increasing or decreasing the neuron’s voltage level.

All of this is built onto a neat purple PCB in the shape of a nerve cell, with external connections on the tips of its dendrites. The neuron’s internal state is made visible by an LED bar graph, giving the user an immediate feel for what’s going on inside the network. Multiple neurons can be connected together to form reasonably complex networks that can implement things like oscillators or logic functions, examples of which are shown on the project’s GitHub page.

The Lu.i project is a great way to teach the basics of neuroscience, turning dry differential equations into a neat display of signals racing around a network. Neurons are fascinating things that we’re learning more about every day, enabling things like brain-computer interfaces and neuromorphic computing.

Liquid Neural Networks Do More With Less

April 30, 2023 by Joseph Long 14 Comments

[Ramin Hasani] and colleague [Mathias Lechner] have been working with a new type of Artificial Neural Network called Liquid Neural Networks, and presented some of the exciting results at a recent TEDxMIT.

Liquid neural networks are inspired by biological neurons to implement algorithms that remain adaptable even after training. [Hasani] demonstrates a machine vision system that steers a car to perform lane keeping with the use of a liquid neural network. The system performs quite well using only 19 neurons, which is profoundly fewer than the typically large model intelligence systems we’ve come to expect. Furthermore, an attention map helps us visualize that the system seems to attend to particular aspects of the visual field quite similar to a human driver’s behavior.

Mathias Lechner and Ramin Hasani — [Mathias Lechner] and [Ramin Hasani]

The typical scaling law of neural networks suggests that accuracy is improved with larger models, which is to say, more neurons. Liquid neural networks may break this law to show that scale is not the whole story. A smaller model can be computed more efficiently. Also, a compact model can improve accountability since decision activity is more readily located within the network. Surprisingly though, liquid neural network performance can also improve generalization, robustness, and fairness.

A liquid neural network can implement synaptic weights using nonlinear probabilities instead of simple scalar values. The synaptic connections and response times can adapt based on sensory inputs to more flexibly react to perturbations in the natural environment.

We should probably expect to see the operational gap between biological neural networks and artificial neural networks continue to close and blur. We’ve previously presented on wetware examples of building neural networks with actual neurons and ever advancing brain-computer interfaces.

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This Week In Security: Session Puzzling, Session Keys, And Speculation

April 28, 2023 by Jonathan Bennett 7 Comments

Last week we briefly mentioned a vulnerability in the Papercut software, and more details and a proof of concept have been published. The vulnerability is one known as session puzzling. That’s essentially where a session variable is used for multiple purposes, or gets incorrectly set. In Papercut, it was possible to trigger the SetupCompleted class on a server that had already finished that initial setup process. And part of SetupCompleted validated the session of the current user. In a normal first-setup case, that might make sense, but as anyone could trigger that code, it allowed anonymous users to jump straight to admin.

The other half of the exploit leverages the “print script” feature, which lets admins write code that runs on printing. A simple java.lang.Runtime.getRuntime().exec('calc.exe'); does the trick to jump from web interface to remote code execution. The indicators of compromise are reasonable generic, including User "admin" logged into the administration interface. and Admin user "admin" modified the print script on printer "".. A Shodan search turns up around 1,700 Papercut servers accessible from the Internet, which prompts the painfully obvious observation that your internal print auditing solution’s web interface definitely should not be exposed online.

Apache Superset

Superset is a nifty data visualization tool for showing charts, graphs, and all sorts of pretty data sets on a dashboard. It also has some weirdness with using web sessions for user management. The session is stored on the user side in a cookie, signed with a secret key. This works great, unless the key used is particularly weak. And guess what, the default configuration of Superset uses a pre-populated secret key. thisismysecretkey is arguably a bad key to start with, but it turns out it’s also shared by more than 70% of the accessible Superset servers.

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