Keeping Badgers At Bay With Tensorflow

Human-animal conflict is always a contentious issue, and finding ways to prevent damage without causing harm to the animals often requires creative solutions. [James Milward] needed a humane method to stop badgers and foxes from uprooting his garden, leading him to create the Furbinator 3000, a system that combines computer vision with audio deterrents..

[James] initially tried using scent repellents (which were ignored) and blocking access to his garden (resulting in more digging), but found some success with commercial ultrasonic audio repellent devices. However, these had to be manually turned off during the day to avoid annoying activation of the PIR motion sensors by [James] and his family, and the integrated solar panels couldn’t keep up with the load.

This presented a good opportunity to try his hand at practical machine vision. He already had a substantial number of sample images from the Ring cameras in his garden, which he turned into a functional TensorFlow Lite model with about 2.5 hours of training. He linked it with event-activated RTSP streams from his Ring cameras using the ring-mqtt library. To minimize false positives on stationary objects, he incorporated a motion filter into the processing pipeline. When it identifies a fox or badger with reasonable accuracy, it generates an MQTT event.

[James] modified the ultrasonic devices so they would react to these events using an ESP8266-based WeMos D1 Mini Pro development board and added an external 5 V power supply for sustained operation. All development was performed in a Docker container which simplified deployment on a Raspberry Pi 4.

After implementing the system, [James] woke up to the satisfying sight of his garden remaining untouched overnight, a victory that even earned him some coverage by the BBC.

Thanks for the tip [Laurent]!

Autonomous Wheelchair Lets Jetson Do The Driving

Compared to their manual counterparts, electric wheelchairs are far less demanding to operate, as the user doesn’t need to have upper body strength normally required to turn the wheels. But even a motorized wheelchair needs some kind of input from the user to control it, which still may pose a considerable challenge depending on the individual’s specific abilities.

Hoping to improve on the situation, [Kabilan KB] has developed a self-driving electric wheelchair that can navigate around obstacles by feeding the output of an Intel RealSense Depth Camera and LiDAR module into a Jetson Nano Developer Kit running OpenCV. To control the actual motors, the Jetson is connected to an Arduino which in turn is wired into a common L298N motor driver board.

As [Kabilan] explains on the NVIDIA Blog, he specifically chose off-the-shelf components and the most affordable electric wheelchair he could find to bring the total cost of the project as low as possible. An undergraduate from the Karunya Institute of Technology and Sciences in Coimbatore, India, he notes that this sort of assistive technology is usually only available to more affluent patients. With his cost-saving measures, he hopes to address that imbalance.

While automatic obstacle avoidance would already be a big help for many users, [Kabilan] imagines improved software taking things a step further. For example, a user could simply press a button to indicate which room of the house they want to move to, and the chair could drive itself there automatically. With increasingly powerful single-board computers and the state of open source self-driving technology steadily improving, it’s not hard to imagine a future where this kind of technology is commonplace.

Multi-Year Doorbell Project

Camera modules for the Raspberry Pi became available shortly after its release in the early ’10s. Since then there has been about a decade of projects eschewing traditional USB webcams in favor of this more affordable, versatile option. Despite the amount of time available there are still some hurdles to overcome, and [Esser50k] has some supporting software to drive a smart doorbell which helps to solve some of them.

One of the major obstacles to using the Pi camera module is that it can only be used by one process at a time. The PiChameleon software that [Esser50k] built is a clever workaround for this, which runs the camera as a service and allows for more flexibility in using the camera. He uses it in the latest iteration of a smart doorbell and intercom system, which uses a Pi Zero in the outdoor unit armed with motion detection to alert him to visitors, and another Raspberry Pi inside with a touch screen that serves as an interface for the whole system.

The entire build process over the past few years was rife with learning opportunities, including technical design problems as well as experiencing plenty of user errors that caused failures as well. Some extra features have been added to this that enhance the experience as well, such as automatically talking to strangers passing by. There are other unique ways of using machine learning on doorbells too, like this one that listens for a traditional doorbell sound and then alerts its user.

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An automatic laser turret playing with a cat.

Entertain Your Cats Automatically With LazerPaw

Most of us would agree that kittens are very cute, but require lots of attention in return. What would you do if you adopted three abandoned cats but didn’t have all day to play with them? [Hoani Bryson] solved his problem by building LazerPaw — an autonomous, safe way to let your cats chase lasers.

Having recently tinkered with computer vision in the form of OpenCV, [Hoani] decided he would make a laser turret for his cats to play with. An infrared camera, used so that the LazerPaw works in the dark, is mounted to the laser and the Raspberry Pi. These electronics are then mounted on a servo-based pan/tilt module, which is in turn mounted with two smartphone clamps to the ceiling. That way, when the cats chase the laser, they will be looking away from the beam source. Additionally, if the device is aiming directly at a cat, the laser is turned off. Finally, [Hoani] added some NeoPixels with an Arduino-based controller for extra hacker vibes.

The LazerPaw’s software takes in a 30 FPS stream from a webcam, scales it down for performance, and applies a threshold filter to it. When a black pixel, which is assumed to be a cat, is detected, it “pushes” the camera away from it depending on how close to the laser it is. The effect of this is that every time a cat catches up to the laser, it moves away again. The processed images are also sent to an interactive website for remote cat playtime. Finally, there is also a physical start button so you don’t need WiFi to use it.

Is your cat more of a sunbather than a deadly murder beast? Maybe it’ll like this cat chair that follows the sun.

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Smart Garbage Trucks Help With Street Maintenance

If you’ve ever had trouble with a footpath, bus stop, or other piece of urban infrastructure, you probably know the hassles of dealing with a local council. It can be incredibly difficult just to track down the right avenue to report issues, let alone get them sorted in a timely fashion.

In the suburban streets of one Australian city, though, that’s changing somewhat. New smart garbage trucks are becoming instruments of infrastructure surveillance, serving a dual purpose that could reshape urban management. Naturally, though, this new technology raises issues around ethics and privacy.

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Several video clips of a robot arm manipulating objects in a kitchen environment, demonstrating some of the 12 generalized skills

RoboAgent Gets Its MT-ACT Together

Researchers at Carnegie Mellon University have shared a pre-print paper on generalized robot training within a small “practical data budget.” The team developed a system that breaks movement tasks into 12 “skills” (e.g., pick, place, slide, wipe) that can be combined to create new and complex trajectories within at least somewhat novel scenarios, called MT-ACT: Multi-Task Action Chunking Transformer. The authors write:

Trained merely on 7500 trajectories, we are demonstrating a universal RoboAgent that can exhibit a diverse set of 12 non-trivial manipulation skills (beyond picking/pushing, including articulated object manipulation and object re-orientation) across 38 tasks and can generalize them to 100s of diverse unseen scenarios (involving unseen objects, unseen tasks, and to completely unseen kitchens). RoboAgent can also evolve its capabilities with new experiences.

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Spy Tech: Unshredding Documents

Bureaucracies generate paper, usually lots of paper. Anything you consider private — especially anything that could get you in trouble — should go in a “burn box” which is usually a locked trash can that is periodically emptied into an incinerator. However, what about a paper shredder? Who hasn’t seen a movie or TV show where the office furiously shreds papers as the FBI, SEC, or some other three-letter-agency is trying to crash the door down?

That might have been the scene in the late 1980s when Germany reunified. The East German Ministry of State Security — known as the Stasi — had records of unlawful activity and, probably, information about people of interest. The staff made a best effort to destroy these records, but they did not quite complete their task.

The collapsing East German government ordered documents destroyed, and many were pulped or burned. However, many of the documents were shredded by hand, stuffed into bags, and were awaiting final destruction. There were also some documents destroyed by the interim government in 1990. Today there are about 16,000 of these bags remaining, each with 2,500 to 3,000 pieces of pages in them.

Machine-shredded documents were too small to recover, but the hand-shredded documents should be possible to reconstruct. After all, they do it all the time in spy movies, right? With modern computers and vision systems, it should be a snap.

You’d think so, anyway.

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