In the time since the Hackaday Prize was first run it has nurtured an astonishing array of projects from around the world, and brought to the fore some truly exceptional winners that have demonstrated world-changing possibilities. This year it has been extended to a new frontier with the launch of the Hackaday Prize China (Chinese language, here’s a Google Translate link), allowing engineers, makers, and inventors from that country to join the fun. We’re pleased to announce the finalists, from which a winner will be announced in Shenzhen, China on November 23rd. If you’re in Shenzen area, you’re invited to attend the award ceremony!
All six of these final project entries have been translated into English to help share information about projects across the language barrier. On the left sidebar of each project page you can find a link back to the original Chinese language project entry. Each presents a fascinating look into what people in our global community can produce when they live at the source of the component supply chain. Among them are a healthy cross-section of projects which we’ll visit in no particular order. Let’s dig in and see what these are all about!
Continue reading “Hackaday Prize China Finalists Announced”
Consider the complexity of the appendages sitting at the end of your arms. The human hands contain over a quarter of the entire complement of bones in the body, use dozens of muscles both in the hand itself and extending up the forearm, and are capable of
almost infinite variance in the movements they can create. They are exquisite machines.
And yet when it comes to virtual reality, most simulations treat the hands like inert blobs. That may be partly due to their complexity; doing motion capture from so many joints can be computationally challenging. But this pressure-sensitive hand motion capture rig aims to change that. The product of an undergraduate project by [Leslie], [Hunter], and [Matthew], the idea was to provide an economical and effective way to capture gestures for virtual reality simulators, which generally focus on capturing large motions from the whole body.
The sensor consists of a sandwich of polyurethane foam with strain gauge sensors embedded within. The user slips his or her hand into the foam and rests the fingers on the sensors. A Teensy and twenty lines of code translate finger motions within the sandwich into five axes of joystick movement, which is then sent to Unreal Engine, where finger motions were translated to a 3D-model of a hand to play a VR game of “Rock, Paper, Scissors.”
[Leslie] and her colleagues have a way to go on this; testers complained that the flat hand posture was unnatural, and that the foam heated things up quickly. Maybe something more along the lines of these gesture-capturing gloves would work?
Robots that can jump have been seen before, but a robot that jumps all the time is a little different. Salto-1P is a one-legged jumping robot at UC Berkeley, and back in 2017 it demonstrated the ability to hop continuously with enough control to keep itself balanced. Since then it has been taught some new tricks; having moved beyond basic stability it can now jump around and upon things with an impressive degree of control.
Key to doing this is the ability to plant its single foot exactly where it wants, which allows for more complex behaviors such as hopping onto and across different objects. [Justin Yim] shows this off in the video embedded below, which demonstrates the Salto-1P bouncing around in a remarkably controlled fashion, even on non-ideal things like canted surfaces. Two small propellers allow the robot to twist in midair, but all the motive force comes from the single leg.
Continue reading “One-Legged Jumping Robot Shows That Control Is Everything”
[Chordata] is making a motion capture system for everyone to build and so far the results are impressive, enough to have been a finalist in the Hackaday Human Computer Interface Challenge. It started a few years ago as one person’s desire to capture a digital performance of a dancer on a stage and has grown into a community of contributors. The board files and software have just been released as alpha along with some instructions for making it work, though more detailed documentation is on the way.
Fifteen sensor boards, called K-Ceptors, are attached to various points on the body, each containing an LSM9DS1 IMU (Inertial Measurement Unit). The K-Ceptors are wired together while still allowing plenty of freedom to move around. Communication is via I2C to a Raspberry Pi. The Pi then sends the collected data over WiFi to a desktop machine. As you move around, a 3D model of a human figure follows in realtime, displayed on the desktop’s screen using Blender, a popular, free 3D modeling software. Of course, you can do something else with the data if you want, perhaps make a robot move? Check out the overview and the performance by a clearly experienced dancer putting the system through its paces in the video below.
As a side note, the latest log entry on their Hackaday.io page points out that whenever changes are made to the K-Ceptor board, fifteen of them need to be made in order to try it out. To help with that, they show the testbed they made for troubleshooting boards as soon as they come out of the oven.
Continue reading “A Motion Capture System For Everyone”
Some people really put a lot of effort into rigging the system. Why spend years practicing a skill and honing your technique to hit a perfect bullseye in darts when you can spend the time building an incredibly complicated auto-bullseye dartboard that’ll do it for you?
In fairness, what [Mark Rober] started three years ago seemed like a pretty simple task. He wanted to build a rig to move the dartboard’s bullseye to meet the predicted impact of any throw. Seems simple, but it turns out to be rather difficult, especially when you choose to roll your own motion capture system.
That system, built around the Nvidia Jetson TX1, never quite gelled, a fact which unfortunately burned through the first two years of the project. [Mark] eventually turned to the not inexpensive Vicon Vantage motion capture system with six IR cameras. A retroreflector on the non-regulation dart is tracked by the system and the resulting XY data is fed into MATLAB to calculate the parabolic path of the dart. An XY-gantry using six steppers quickly shifts the board so the bullseye is in the right place to catch the incoming dart.
It’s a huge amount of work and a lot of money to spend, but the group down at the local bar seemed to enjoy it. We wonder if it can be simplified, though. Perhaps tracking just the thrower’s motions with an IMU-based motion capture system and extrapolating the impact point would work.
Continue reading “Dartboard Watches Your Throw; Catches Perfect Bullseyes”
[Alvaro Ferrán Cifuentes] has built the coolest motion capture suit that we’ve seen outside of Hollywood. It’s based on tying a bunch of inertial measurement units (IMUs) to his body, sending the data to a computer, and doing some reasonably serious math. It’s nothing short of amazing, and entirely doable on a DIY budget. Check out the video below the break, and be amazed.
Cellphones all use IMUs to provide such useful functions as tap detection and screen rotation information. This means that they’ve become cheap. The ability to measure nine degrees of freedom on a tiny chip, for chicken scratch, pretty much made this development inevitable, as we suggested back in 2013 after seeing a one-armed proof-of-concept.
But [Alvaro] has gone above and beyond. Everything is open source and documented on his GitHun. An Arduino reads the sensor boards (over multiplexed I2C lines) that are strapped to his limbs, and send the data over Bluetooth to his computer. There, a Python script takes over and passes the data off to Blender which renders a 3D model to match, in real time.
All of this means that you could replicate this incredible project at home right now, on the cheap. We have no idea where this is heading, but it’s going to be cool.
Continue reading “Amazing IMU-based Motion Capture Suit Turns You Into A Cartoon”
Listening tests reveal significant sound quality differences between various digital music storage technologies. Finally the audiophile press is tackling the important questions. This listening test looks at the difference between two four-bay NAS boxes, with one making the piano on Scherzo and Trio from Penguin Café Orchestra’s Union Cafe sound more Steinway-like, while another NAS makes it sound more like a Bosendörfer. Yes, your choice of digital storage medium can change the timbre of a piano. Another gem: “Additionally, the two units also had different processor architectures, which might also affect perceived audible differences.” There must be a corollary to Poe’s Law when it comes to audiophiles…
[10p6] has begun a project that can play every old Atari cartridge. Right now it’s just a few bits of plastic that fits every non-Jaguar Atari cartridge, but it’s a start.
The Android IMSI-Catcher Detector. You’ve heard about Stingrays, devices used by law enforcement that are basically fake cell towers. These Stingrays downgrade or disable the encryption present in all cellphones, allowing anyone, with or without a warrant, to listen in on any cell phone conversation. Now there’s an effort to detect these Stingrays. It’s open source, and they’re looking for volunteers.
[Rob] sent in something that’s the perfect application of projection mapping. It’s called Face Hacking, and it’s pretty much just a motion capture systems, a few projectors, a whole lot of CG work, and just a tiny bit of dubstep. It look cool, but we’re wondering what the applications would be. Theatre or some sort of performance art is the best I can come up with.
A while ago, [4ndreas] saw a 3D printed industrial robot arm. He contacted the guy for the files, but nothing came of that. [4ndreas] did what anyone should do – made his own 3D printable industrial robot arm. The main motors are NEMA 17, and printing this will take a long time. Still, it looks really, really cool.