Hackaday Belgrade: Luka Mustafa on Exploiting IoT Niches

Ecology is a strange discipline. At its most basic, it’s the study of how living things interact with their environment. It doesn’t so much seek to explain how life works, but rather how lives work together. A guiding principle of ecology is that life finds a way to exploit niches, subregions within the larger world with a particular mix of resources and challenges. It’s actually all quite fascinating.

But what does ecology have to do with Luka Mustafa’s talk at the 2018 Hackaday Belgrade Conference? Everything, as it turns out, and not just because Luka and his colleagues put IoT tools on animals and in their environments to measure and monitor them. It’s also that Luka has found a fascinating niche of his own to exploit, one on the edge of technology and ecology. As CEO of Institute IRNAS, a non-profit technology development group in Slovenia, Luka has leveraged his MEng degree, background in ham radio, and interest in LoRaWAN and other wide-area radio networks to explore ecological niches in ways that would have been unthinkable even 10 years ago, let alone in the days when animal tracking was limited by bulky radio collars.

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Robot Maps Rooms with Help From iPhone

The Unity engine has been around since Apple started using Intel chips, and has made quite a splash in the gaming world. Unity allows developers to create 2D and 3D games, but there are some other interesting applications of this gaming engine as well. For example, [matthewhallberg] used it to build a robot that can map rooms in 3D.

The impetus for this project was a robotics company that used a series of robots around their business. The robots navigate using computer vision, but couldn’t map the rooms from scratch. They hired [matthewhallberg] to tackle this problem, and this robot is a preliminary result. Using the Unity engine and an iPhone, the robot can perform in one of three modes. The first is a user-controlled mode, the second is object following, and the third is 3D mapping.

The robot seems fairly easy to construct and only carries and iPhone, a Node MCU, some motors, and a battery. Most of the computational work is done remotely, with the robot simply receiving its movement commands from another computer. There’s a lot going on here, software-wise, and a lot of toolkits and software packages to install and communicate with one another, but the video below does a good job of showing what you’ll need and how it all works together. If that’s all too much, there are other robots with a form of computer vision that can get you started into the world of computer vision and mapping.

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Simple Quadcopter Testbed Clears The Air For Easy Algorithm Development

We don’t have to tell you that drones are all the rage. But while new commercial models are being released all the time, and new parts get released for the makers, the basic technology used in the hardware hasn’t changed in the last few years. Sure, we’ve added more sensors, increased computing power, and improved the efficiency, but the key developments come in the software: you only have to look at the latest models on the market, or the frequency of Git commits to Betaflight, Butterflight, Cleanflight, etc.

With this in mind, for a Hackaday prize entry [int-smart] is working on a quadcopter testbed for developing algorithms, specifically localization and mapping. The aim of the project is to eventually make it as easy as possible to get off the ground and start writing code, as well as to integrate mapping algorithms with Ardupilot through ROS.

The initial idea was to use a Beaglebone Blue and some cheap hobby hardware which is fairly standard for a drone of this size: 1250 kv motors and SimonK ESCs, mounted on an f450 flame wheel style frame. However, it looks like an off-the-shelf solution might be even simpler if it can be made to work with ROS. A Scanse Sweep LIDAR sensor provides point cloud data, which is then munched with some Iterative Closest Point (ICP) processing. If you like math then it’s definitely worth reading the project logs, as some of the algorithms are explained there.

It might be fun to add FPV to this system to see how the mapping algorithms are performing from the perspective of the drone. And just because it’s awesome. FPV is also a fertile area for hacking: we particularly love this FPV tracker which rotates itself to get the best signal, and this 3D FPV setup using two cameras.

Taking First Place at IMAV 2016 Drone Competition

The IMAV (International Micro Air Vehicle) conference and competition is a yearly flying robotics competition hosted by a different University every year. AKAMAV – a university student group at TU Braunschweig in Germany – have written up a fascinating and detailed account of what it was like to compete (and take first place) in 2016’s eleven-mission event hosted by the Beijing Institute of Technology.

AKAMAV’s debrief of IMAV 2016 is well-written and insightful. It covers not only the five outdoor and six indoor missions, but also details what it was like to prepare for and compete in such an intensive event. In their words, “If you share even a remote interest in flying robots and don’t mind the occasional spectacular crash, this place was Disney Land on steroids.”

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Another Kind of Cloud: The Internet of Farts

It’s taken as canon that girls mature faster than boys. In reality, what happens is that boys stop maturing at about age 12 while girls keep going. And nothing tickles the fancy of the ageless pre-teen boy trapped within all men more than a good fart joke. To wit, we present a geolocating fart tracker for your daily commute.

[Michel] is the hero this world needs, and although he seems to have somewhat of a preoccupation with hacks involving combustible gasses, his other non-methane related projects have graced our pages before, like this electrical meter snooper or an IoT lawn mower. The current effort, though, is a bit on the cheekier side.

The goal is to keep track of his emissions while driving, so with a PIC, an ESP8266, a GPS module, and a small LCD display and keyboard, he now has a way to log his rolling flatulence. When the urge overcomes him he simply presses a button, which logs his location and speed and allows him to make certain qualitative notes regarding the event. The data gets uploaded to the cloud every Friday, which apparently allows [Michel] to while away his weekends mapping his results.

It turns out that he mainly farts while heading south, and he’s worried about the implications both in terms of polar ice cap loss and how Santa is going to treat him next month. We’re thinking he’s got a lock on coal — or at least activated charcoal.

Our beef with this project is obvious – it relies on the honor system for input. We really need to see this reworked with an in-seat methane detector to keep [Michel] honest. Until then, stay young, [Michel].

Gift Your Next Robot With the Brain of a Roundworm

A group of developers called [OpenWorm] have mapped the 302 neurons of the Caenorhabditis elegans species of roundworm and created a virtual neural network that can be used to solve all the types of problems a worm might encounter. Which, when you think about it, aren’t much different from those a floor-crawling robots would be confronted with.


In a demo video released by one of the projects founders, [Timothy Busbice], their network is used to control a small Lego-rover equipped with a forward sonar sensor. The robot is able to stop before it hits a wall and determine an appropriate response, which may be to stop, back up, or turn. This is all pretty fantastic when you compare these 302 neural connections to any code you’ve ever written to accomplish the same task! It might be a much more complex route to the same outcome, but its uniquely organic… which makes watching the little Lego-bot fascinating; its stumbling around even looks more like thinking than executing.

I feel obligated to bring up the implications of this project. Since we’re all thinking about it now, let’s all imagine the human brain similarly mapped and able to simulate complex thought processes. If we can pull this off one day, not only will we learn a lot more about how our squishy grey hard drives process information, artificial intelligence will also improve by leaps and bounds. An effort to do this is already in effect, called the connectome project, however since there are a few more connections to map than with the c. elegans’ brain, it’s a feat that is still underway.

The project is called “open”worm, which of course means you can download the code from their website and potentially dabble in neuro-robotics yourself. If you do, we want to hear about your wormy brain bot.

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Hardware-based keyboard remapping

[Nav] wanted to change his keyboard mapping for one particular keyboard, rather than on each operating system. He used an AT90USBKey as a replacement PCB by soldering to all of the contacts on the key matrix. This allows him to remap the keys by following onscreen prompts.

The board enumerates as an HID device, and has a special mode which is accesses by plugging the keyboard in while holding down any key. If a text editor window is active you’ll see prompts from the microcontroller to press a series of keys. This is a routine used to learn how the key matrix is organized, and it’s your opportunity to change how each key is mapped. Since the mapping is saved to EEPROM, you can use any computer to map the keys, then plug the device into a systems that don’t offer software remapping. It could also be useful as a gaming keyboard, assuming there aren’t latency issues

As with the AVR-based arcade controller, this project uses the LUFA package to handle the USB stack.