Simulate Your Robot Before You Build It

[Nurgak] shows how one can use some of the great robotic tools out there to simulate a robot before you even build it. To drive this point home he builds the tutorial off of the easily 3D printable and buildable Robopoly platform.

The robot runs on Robot Operating System at its core. ROS is interesting because of its decentralized and input/output agnostic messaging system. For example, if you leave everything alone but swap out the motor output from actual motors to a simulator, you can see how the robot would respond to any arbitrary input.

[Nurgak] uses another piece of software called V-REP to demonstrate this. V-REP is a simulation suite for robotics and has a few ROS nodes built in. So in order to make a simulated line-following robot, [Nurgak] tells V-REP to send a simulated camera image to the decision making node of the robot in ROS. It then sends the movement messages back to V-REP which drives the pretend robot around.

He runs through a few more examples, proving that it’s entirely possible to become if not a roboticist, at least a really good AI programmer without ever dropping the big money on parts to build a robot.

Pan and Tilt with Dual Controllers

It wasn’t long ago that faced with a controller project, you might shop for something with just the right features and try to minimize the cost. These days, if you are just doing a one-off, it might be just as easy to throw commodity hardware at it. After all, a Raspberry Pi costs less than a nice meal and it is more powerful than a full PC would have been not long ago.

When [Joe Coburn] wanted to make a pan and tilt webcam he didn’t try to find a minimal configuration. He just threw a Raspberry Pi in for interfacing to the Internet and an Arduino in to control two RC servo motors. A zip tie holds the servos together and potentially the web cam, too.

You can see the result in the video below. It is a simple matter to set up the camera with the Pi, send some commands to the Arduino and hook up to the Internet.

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One Man, A Raspberry Pi, and a Formerly Hand Powered Loom

[Fred Hoefler] was challenged to finally do something with that Raspberry Pi he wouldn’t keep quiet about. So he built a machine assist loom for the hand weaver. Many older weavers simply can’t enjoy their art anymore due to the physical strain caused by the repetitive task. Since he had a Pi looking for a purpose, he also had his project.

His biggest requirement was cost. There are lots of assistive looms on the market, but the starting price for those is around ten thousand dollars. So he set the rule that nothing on the device would cost more than the mentioned single board computer. This resulted in a BOM cost for the conversion that came in well under two hundred dollars. Not bad!

The motive parts are simple cheap 12V geared motors off Amazon. He powered them using his own motor driver circuits. They get their commands from the Pi, running Python. To control the loom one can either type in commands into the shell or use the keyboard. There are also some manual switches on the loom itself.

In the end [Fred] met his design goal, and has further convinced his friends that the words Raspberry Pi are somehow involved with trouble.

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Pokemon Go – Bot Edition

A wild Python appeared, and it wants to play Pokemon Go. Python bots are taking over the game when you can’t, and they are good. Since you’re likely to bump into one sooner or later, here’s an overview:

One of the first workable bots and the origin of a lot of (dirty) code, as well as the (not dirty at all) Pokemon Trainer Club client secret, is [Mila432’s] Pokemon Go Bot. One of his initial goals was to make better sense of the API, which worked out better than he hoped.

Not wanting to impetuously destroy the numerous helpful applications that had been built upon the already partially known API, he decided to keep the project off Niantic’s radar. The most recent (and most powerful) version of his bot has not been released. The current version works fine within its limited functionality: Wandering around and looting Pokestops.

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BitCluster Brings a New Way to Snoop Through BitCoin Transactions

Mining the wealth of information in the BitCoin blockchain is nothing new, but BitCluster goes a long way to make sense of the information you’ll find there. The tool was released by Mathieu Lavoie and David Decary-Hetu, PH.D. on Friday following their talk at HOPE XI.

I greatly enjoyed sitting in on the talk which began with some BitCoin basics. The cryptocurrency uses user generated “wallets” which are essentially addresses that identify transactions. Each is established using key pairs and there are roughly 146 million of these wallets in existence now

If you’re a thrifty person you might think you can get one wallet and use it for years. That might be true of the sweaty alligator-skin nightmare you’ve had in your back pocket for a decade now. It’s not true when it comes to digital bits —  they’re cheap (some would say free). People who don’t generate a new wallet for every transaction weaken their BitCoin anonymity and this weakness is the core of BitCluster’s approach.

Every time you transfer BitCoin (BTC) you send the network the address of the transaction when you acquired the BTCs and sign it with your key to validate the data. If you reuse the same wallet address on subsequent transactions — maybe because you didn’t spend all of the wallet’s coins in one transaction or you overpaid and have the change routed back to your wallet. The uniqueness of that signed address can be tracked across those multiple transactions. This alone won’t dox you, but does allow a clever piece of software to build a database of nodes by associating transactions together.

Mathieu’s description of first attempts at mapping the blockchain were amusing. The demonstration showed a Python script called from the command line which started off analyzing a little more than a block a second but by the fourth or fifth blocks hit the process had slowed to a standstill that would never progress. This reminds me of some of the puzzles from Project Euler.

bitcluster-how-it-worksAfter a rabbit hole of optimizations the problem has been solved. All you need to recreate the work is a pair of machines (one for Python one for mondoDB) with the fastest processors you can afford, a 500 GB SSD, 32 GB of RAM (but would be 64 better), Python 64-bit, and at least a week of time. The good news is that you don’t have to recreate this. The 200GB database is available for download through a torrent and the code to navigate it is up on GitHub. Like I said, this type of blockchain sleuthing isn’t new but a powerful open source tool like this is.

Both Ransomware and illicit markets can be observed using this technique. Successful, yet not-so-cautious ransomers sometimes use the same BitCoin address for all payments. For example, research into a 2014 data sample turned up a ransomware instance that pulled in $611k (averaging $10k per day but actually pulling in most of the money during one three-week period). If you’re paying attention you know using the same wallet address is a bad move and this ransomware was eventually shut down.

Illicit markets like Silk Road are another application for BitCluster. Prior research methods relied on mining comments left by customers to estimate revenue. Imagine if you had to guess at how well Amazon was doing reading customer reviews and hoping they mentioned the price? The ability to observe BTC payment nodes is a much more powerful method.

A good illicit market won’t use just one wallet address. But to protect customers they use escrow address and these do get reused making cluster analysis possible. Silk Road was doing about $800k per month in revenue at its height. The bulk of purchases were for less than $500 with only a tiny percentage above $1000. But those large purchases were likely to be drug purchases of a kilo or more. That small sliver of total transactions actually added up to about a third of the total revenue.

bitcluster-logoIt’s fascinating to peer into transactions in this manner. And the good news is that there’s plenty of interesting stuff just waiting to be discovered. After all, the blockchain is a historical record so the data isn’t going anywhere. BitCluster is intriguing and worth playing with. Currently you can search for a BTC address and see total BTC in and out, then sift through income and expense sorted by date, amount, etc. But the tool can be truly great with more development. On the top of the wishlist are automated database updates, labeling of nodes (so you can search “Silk Road” instead of a numerical address), visual graphs of flows, and a hosted version of the query tool (but computing power becomes prohibitive.)

MicroPython on the ESP8266: Kicking the Tires

Scripting languages are for large computers, right? “Real” embedded device work is a hellish, never-ending cycle of code, compile, and re-flash. Well, I used to think so too, but with the proliferation of scripting and other interactive languages to microcontrollers over the last few years, the hurdle to interactive development on the small chips has gotten a lot lower.

On the ESP8266 platform, I’ve tried out NodeMCU’s Lua and ESP8266 BASIC. (For the last half-year, I’ve been using the awesome Mecrisp-Stellaris almost exclusively on the STM32F1xx and F4xx chips, but haven’t dipped into ESP8266 Forth yet.)

NodeMCU is great because it’s got everything you could want built in, and through cloud services it’s easy to get a tailored build made that maximizes free flash memory for your projects. I just don’t dig the asynchronous Lua thing (you might, try it!). ESP BASIC has a different set of libraries, and is missing MQTT for my purposes. Still it’s pretty slick, and worth a look.

So when the MicroPython folks announced that they were releasing the binary builds for the ESP, I thought it was time to give it a spin. I’ve used Python for nearly twelve years now, so it’s like a comfortable shoe for me. Would MicroPython be the same on the ESP8266? The short answer is yes and no.

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Hackaday Prize Entry: Open Source FFT Spectrum Analyzer

Every machine has its own way of communicating with its operator. Some send status emails, some illuminate, but most of them vibrate and make noise. If it hums happily, that’s usually a good sign, but if it complains loudly, maintenance is overdue. [Ariel Quezada] wants to make sense of machine vibrations and draw conclusions about their overall mechanical condition from them. With his project, a 3-axis Open Source FFT Spectrum Analyzer he is not only entering the Hackaday Prize 2016 but also the highly contested field of acoustic defect recognition.

open_fft_machineFor the hardware side of the spectrum analyzer, [Ariel] equipped an Arduino Nano with an ADXL335 accelerometer, which is able to pick up vibrations within a frequency range of 0 to 1600 Hz on the X and Y axis. A film container, equipped with a strong magnet for easy installation, serves as an enclosure for the sensor. The firmware [Ariel] wrote is an efficient piece of code that samples the analog signals from the accelerometer in a free running loop at about 5000 Hz. It streams the digitized waveforms to a host computer over the serial port, where they are captured and stored by a Python script for further processing.

From there, another Python script filters the captured waveform, applies a window function, calculates the Fourier transform and plots the spectrum into a graph. With the analyzer up and running, [Ariel] went on testing the device on a large bearing of an arbitrary rotating machine he had access to. A series of tests that involved adding eccentric weights to the rotating shaft shows that the analyzer already makes it possible to discriminate between different grades of imbalance.

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