Hackaday Links: September 17, 2023

September 17, 2023 by Dan Maloney 4 Comments

OK, it’s official — everyone hates San Francisco’s self-driving taxi fleet. Or at least so it seems, if this video of someone vandalizing a Cruise robotaxi is an accurate reflection of the public’s sentiment. We’ve been covering the increasingly fraught relationship between Cruise and San Franciscans for a while now — between their cabs crashing into semis and being used for — ahem — non-transportation purposes, then crashing into fire trucks and eventually having their test fleet cut in half by regulators, Cruise really seems to be taking it on the chin.

And now this video, which shows a wannabe Ninja going ham on a Cruise taxi stopped somewhere on the streets of San Francisco. It has to be said that the vandal doesn’t appear to be doing much damage with what looks like a mason’s hammer; except for the windshield and side glass and the driver-side mirror — superfluous for a self-driving car, one would think — the rest of the roof-mounted lidars and cameras seem to get off lightly. Either Cruise’s mechanical engineering is better than their software engineering, or the neo-Luddite lacks the upper body strength to do any serious damage. Or maybe both.

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Browser-Based Robot Dog Simulator In ~800 Lines Of Code

August 5, 2023 by Donald Papp 9 Comments

[Sergii] has been learning about robot simulation and wrote up a basic simulator for a robodog platform: the Unitree A1. It only took about 800 lines of code to do so, which probably makes it a good place to start if one is headed in a similar direction.

Right now, [Sergii]’s simulator is an interactive physics model than runs in the browser. Software-wise, once the model of the robot exists the Rapier JavaScript physics engine takes care of the physics simulation. The robot’s physical layout comes from the manufacturer’s repository, so it doesn’t need to be created from scratch.

To make the tool useful, the application has two models of the robot, side by side. The one on the left is the control model, and has interactive sliders for limb positions. All movements on the control model are transmitted to the model on the right, which is the simulation model, setting the pose. The simulation model is the one that actually models the physics and gravity of all the desired motions and positions. [Sergii]’s next step is to use the simulator to design and implement a simple walking gait controller, and we look forward to how that turns out.

If Unitree sounds familiar to you, it might be because we recently covered how an unofficial SDK was able to open up some otherwise-unavailable features on the robodogs, so check that out if you want to get a little more out of what you paid for.

Kino Wheels Gives You A Hand Learning Camera Operation

March 28, 2023 by Dan Maloney 4 Comments

Have you ever watched a movie or a video and really noticed the quality of the camera work? If you have, chances are the camera operator wasn’t very skilled, since the whole point of the job is to not be noticed. And getting to that point requires a lot of practice, especially since the handwheel controls for professional cameras can be a little tricky to master.

Getting the hang of camera controls is the idea behind [Cadrage]’s Kino Wheels open-source handwheels. The business end of Kino Wheels is a pair of DIN 950 140mm spoked handwheels — because of course there’s a DIN standard for handwheels. The handwheels are supported by sturdy pillow block bearings and attached to 600 pulse/rev rotary encoders, which are read by an Arduino Mega 2560. The handwheels are mounted orthogonal to each other in a suitable enclosure; the Pelican-style case shown in the build instructions seems like a perfect choice, but it really could be just about anything.

To use Kino Wheels, [Cadrage] offers a free camera simulator for Windows. Connected over USB, the wheels control the pan and tilt axes of a simulated camera in an animated scene. The operator-in-training uses the wheels to keep the scene composed properly while following the action. A little bit of the simulation is shown in the brief video below, along with some of the build details.

While getting camera practice is the point of the project, that’s not to say Kino Wheels couldn’t be retasked. With a little work, these could be used to actually control at least a couple of axes of a motion control rig, or maybe even to play Quake.

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Hackaday Links: February 26, 2023

February 26, 2023 by Dan Maloney 32 Comments

It’s probably safe to say that most of us have had enough of the Great Balloon Follies to last the rest of 2023 and well beyond. It’s been a week or two since anything untoward was spotted over the US and subsequently blasted into shrapnel, at least that we know of, so we can probably put this whole thing behind us.

But as a parting gift, we present what has to be the best selfie of the year — a photo by the pilot of a U-2 spy plane of the balloon that started it all. Assuming no manipulation or trickery, the photo is remarkable; not only does it capture the U-2 pilot doing a high-altitude flyby of the balloon, but it shows the shadow cast by the spy plane on the surface of the balloon.

The photo also illustrates the enormity of this thing; someone with better math skills than us could probably figure out the exact size of the balloon from the apparent size of the U-2 shadow, in fact.

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OpenSPICE: A Portable Python Circuit Simulator

February 14, 2023 by Dave Rowntree 22 Comments

[Roman Parise] and [Georgios Is. Detorakis] have created OpenSPICE a fork of the PySpice project, adding a new simulation engine written entirely in Python. This enables the same PySpice simulations to be executed on any platform that runs python (which we reckon is quite a few!) whilst leveraging the full power of the python infrastructure. Since it is a fork — for supported platforms — you can also run your simulations upon Ngspice as well as Xyce, giving options for scaling up to larger systems when required, but importantly without having to recreate your circuit from scratch.

The OpenSPICE simulator first converts the parsed netlist into a set of data structures that represent the equations describing the various parts of the system. These are then in turn passed along the scipy library “optimize.root” function which solves the system, generating a list of branch currents and node voltages. The output of the simulation is a numpy array, which can be further processed and visualized with the mathplotlib library. All pretty standard stuff in python circles. Since this is based upon PySpice, it’s also possible to use KiCAD netlists, so you have a nice way to enter those schematics. We’ve not dug into this much yet, but support for the vast libraries of spice models out there in circulation would be high up on our wish list if it already can’t handle this. This scribe will most definitely be checking this out, as LTSpice whilst good, is a bit of a pain to use and does lack the power of a Python backend!

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Microsoft’s New Simulator Helps Train Drone AIs

August 11, 2022 by Lewin Day 15 Comments

Testing any kind of project in the real world is expensive. You have to haul people and equipment around, which costs money, and if you break anything, you have to pay for that too! Simulation tends to come first. Making mistakes in a simulation is much cheaper, and the lessons learned can later be verified in the real world. If you want to learn to fly a quadcopter, the best thing to do is get some time behind the sticks of a simulator before you even purchase anything with physical whirly blades.

Oddly enough, the same goes for AI. Microsoft built a simulation product to aid the development of artificial intelligence systems for drones by the name of Project AirSim. It aims to provide a comprehensive environment for the testing of drone AI systems, making development faster, cheaper, and more practical.

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Hackaday Links: July 17, 2022

July 17, 2022 by Dan Maloney 9 Comments

Webb’s first deep-field image. Source: NASA

The folks at NASA are taking a well-deserved victory lap this week after the splashy reveal of the first scientific images from the James Webb Space Telescope. As we expected, the first public release included a lot of comparisons to images obtained from Hubble, as the general public understandably sees Webb as the successor to the venerable space telescope, now in its third decade of service. So for a “let’s see what this baby can do” image, they turned Webb loose on a tiny patch of sky in the southern hemisphere containing galactic cluster SMACS 0723, and sent back images and spectroscopic data from galaxies up to 13 billion light years away. There are plenty of analyses of Webb’s deep field and the other images in the first release, but we particularly liked the takes by both Anton Petrov and Dr. Becky. They both talk about the cooler scientific aspects of these images, and how Webb is much more than just a $10 billion desktop image generator.

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