Tracking Ants And Zapping Them With Lasers

Thanks to the wonders of neural networks and machine learning algorithms, it’s now possible to do things that were once thought to be inordinately difficult to achieve with computers. It’s a combination of the right techniques and piles of computing power that make such feats doable, and [Robert Bond’s] ant zapping project is a great example.

The project is based around an NVIDIA Jetson TK1, a system that brings the processing power of a modern GPU to an embedded platform. It’s fitted with a USB camera, that is used to scan its field of view for ants. Once detected, thanks to a little OpenCV magic, the coordinates of the insect are passed to the laser system. Twin stepper motors are used to spin mirrors that direct the light from a 5 mW red laser, which is shined on the target. If you’re thinking of working on something like this we highly recommend using galvos to direct the laser.

Such a system could readily vaporize ants if fitted with a more powerful laser, but [Robert] decided to avoid this for safety reasons. Plus, the smell wouldn’t be great, and nobody wants charred insect residue all over the kitchen floor anyway. We’ve seen AIs do similar work, too – like detecting naughty cats for security reasons.

Continue reading “Tracking Ants And Zapping Them With Lasers”

Learn Programming From Ants

Humans and insects think on a different scale, but entomologists study the behavior of these little organisms, so they’re not a complete mystery. There isn’t much intelligence in a single ant or a cubic millimeter of gray matter, but when they all start acting together, you get something greater than the sum of the parts. It is easy to fall into the trap of putting all the intelligence or programming into a single box since that’s how we function. Comparatively, itty-bitty brains, like microcontrollers and single-board computers are inexpensive and plentiful. Enter swarm mentality, and new tasks become possible.

[Kevin Hartnett] talks about a paper researching the simple rules which govern army ants who use their bodies as bridges when confronted with a gap in their path. Anyone with a ruler and a map can decide the shortest route between two places, but army ants perform this optimization from the ground, real-time, and with only a few neurons at their disposal. Two simple rules control bridge building behavior, and that might leave some space in the memory banks of some swarm robots.

A simpler example of swarm mentality could be robots which drive forward anytime they sense infrared waves from above. In this way, anyone watching the swarm could observe when an infrared light was present and where it was directed. You could do the same with inexpensive solar-powered toy cars, but we can already see visible light.

We’re not saying ants should be recruited to control robots, but we’re not objecting to the humane treatment of cyborg bugs either. We’ve been looking into swarm robots for a long time.

Thanks for the tip, [JRD].

Continue reading “Learn Programming From Ants”

DIY Graphene Putty Makes Super Sensitive Sensor

It is sort of an electronics rule 34 that if something occurs, someone needs to sense it. [Bblorgggg], for reasons that aren’t immediately obvious, needs to sense ants moving over trees. No kidding. How are you going to do that? His answer was to use graphene.

Actually, his super sensitive sensors mix graphene in Silly Putty, an unlikely combination that he tried after reading (on Hackaday, no less) about similar experiments at Trinity College resulting in Gputty. The Gputty¬†was highly sensitive to pressure, and so it appears is his DIY version called Goophene. At Trinity they claimed to be able to record the footsteps of a spider, so detecting ant stomping didn’t seem too far-fetched. You can see a video of the result, below.

Continue reading “DIY Graphene Putty Makes Super Sensitive Sensor”