When you’re lucky enough to have a dog in your life, you tend to overlook some of the more one-sided aspects of the relationship. While you are severely restrained with regard to where you eliminate your waste, your furry friend is free to roam the yard and dispense his or her nuggets pretty much at will, and fully expect you to follow along on cleanup duty. See what we did there?
And so dog people sometimes rebel at this lopsided power structure, by leaving the cleanup till later — often much, much later, when locating the offending piles can be a bit difficult. So naturally, we now have this poop-shooting laser turret to helpfully guide you through your backyard cleanup sessions. It comes to us from [Caleb Olson], who leveraged his recent poop-posture monitor as the source of data for where exactly in the yard each deposit is located. To point them out, he attached a laser pointer to a cheap robot arm, and used OpenCV to help line up the bright green spot on each poop.
But wait, there’s more. [Caleb]’s code also optimizes his poop patrol route, minimizing the amount of pesky walking he has to do to visit each pile. And, the same pose estimation algorithm that watches the adorable [Twinkie] make her deposits keeps track of which ones [Caleb] stoops by, removing each from the worklist in turn. So now instead of having a dog control his life, he’s got a dog and a computer running the show. Perfect.
We joke, because poop, but really, this is a pretty neat exercise in machine learning. It does seem like the robot arm was bit overkill, though — we’d have thought a simple two-servo turret would have been pretty easy to whip up.
Continue reading “Point Out Pup’s Packages With This Poop-Shooting Laser”
[Caleb] shares a problem with most dog owners. Dogs leave their… byproducts…all over your yard. Some people pick it up right away and some just leave it. But what if your dog has run of the yard? How do you know where these piles are hiding? A security camera and AI image detection is the answer, but probably not the way that you think.
You might think as we did that you could train the system to recognize the–um–piles. But instead, [Caleb] elected to have the AI do animal pose estimation to detect the dog’s posture while producing the target. This is probably easier than recognizing a nondescript pile and then it doesn’t matter if it is, say, covered with snow.
Continue reading “AI Camera Knows Its S**t”
It’s a bit icky reading between the lines on this one… but it’s a fascinating experiment! In his latest Applied Science video, [Ben Krasnow] tries to measure how efficient the human body is at getting energy from food by accurately measuring what he put in and what comes out of his body.
The jumping off point for this experiment is the calorie count on the back of food packaging. [Ben] touches on “bomb calorimetry” — the process of burning foodstuff in an oxygen-rich environment and measuring the heat given off to establish how much energy was present in the sample. But our bodies are flameless… can we really extract similar amounts of energy as these highly controlled combustion chambers? His solution is to measure his body’s intake by eating nothing but Soylent for a week, then subjects his body’s waste to the bomb calorimetry treatment to calculate how much energy was not absorbed during digestion. (He burned his poop for science, and made fun of some YouTubers at the same time.)
The test apparatus is a cool build — a chunk of pipe with an acrylic/glass laminated window that has a bicycle tire value for pressurization, a pressure gauge, and electrodes to spark the combustion using nichrome wire and cotton string. It’s shown above, burning a Goldfish® cracker but it’s not actually measuring the energy output as this is just a test run. The actual measurements call for the combustion chamber to be submerged in an insulated water bath so that the temperature change can be measured.
Now to the dirty bits. [Ben] collected fecal matter and freeze-dried it to ready it for the calorimeter. His preparation for the experiment included eating nothing but Soylent (a powdered foodstuff) to achieve an input baseline. The problem is that he measures the fecal matter to have about 75% of the calories per gram compared to the Soylent. Thinking on it, that’s not surprising as we know that dung must have a high caloric level — it burns and has been used throughout history as a source of warmth among other things. But the numbers don’t lead to an obvious conclusion and [Ben] doesn’t have the answer on why the measurements came out this way. In the YouTube comments [Bitluni] asks the question that was on our minds: how do you correlate the volume of the input and output? Is comparing 1g of Soylent to 1g of fecal matter a correct equivalency? Let us know what you think the comments below.
The science of poop is one of those 8th-grade giggle topics, but still totally fascinating. Two other examples that po
op to mind are our recent sewage maceration infrastructure article and the science of teaching robot vacuums to detect pet waste.
Continue reading “Ben Krasnow Measures Human Calorie Consumption By Collecting The “Output””
NASA is looking for a few good men and women to solve an upcoming problem. Astronauts will soon be venturing outward beyond Earth orbit. If the spacecraft cabin should depressurize then they’ll have to put on their spacesuits and may have to keep them on for up to six days. During that time something will have to handle the resulting urine, fecal, and menstrual waste, all without the astronauts use of their hands. And that’s where you come in.
NASA is having a space poop challenge. The current system of an adult diaper won’t last six days. Your job, should you choose to accept it, is to design a system that will move the waste away from the skin where it can cause infection. Continue reading for the rather unique requirements.
Continue reading “NASA Wants SpacePoop Hackers”
Imagine this: you come home after a day at work. As you open the door, your nose is the first alert that something is very, very wrong. Instead of the usual house smell, your nose is assaulted with the distinctive aroma that means your dog had an accident. The smell is stronger though — as if Fido brought over a few friends and they all had a party. Flipping the lights on, the true horror is revealed to you. This was a team effort, but only one dog was involved.
At some point after the dog’s deed, Roomba, your robot vacuum, took off on its scheduled daily run around the house. The plucky little robot performed its assigned duties until it found the mess. The cleaning robot then became an agent of destruction, smearing a foul smelling mess throughout the space it was assigned to clean. Technology sometimes has unintended consequences. This time, your technology has turned against you.
This scene isn’t a work of fiction. For a select few families, it has become an all too odoriferous reality just begging for a clever fix.
Continue reading “Roomba Vs Poop: Teaching Robots To Detect Pet Mess”
[Eric] is well on his way to making one of the less pleasant chores of pet ownership a bit easier with his dog tracking system. The dog tracker is actually a small part of [Eric’s] much larger OpenHAB system, which we featured back in July.
As a dog owner, [Eric] hates searching the yard for his pet’s droppings. He had been planning a system to make this easier, and a local hackerspace event provided just the opportunity to flesh his ideas out. The Dog Tracker’s primary sensor is a GPS. Most dogs remain motionless for a few seconds while they go about their business. [Eric’s] Arduino-frgbased system uses this fact, coupled with a tilt sensor to determine if the family pet has left any presents.
The tracker relays this information to the home base station using a HopeRF RFM69 transceiver. The RFM69 only has about a 900 foot range, so folks with larger properties will probably want to spring for a cellular network based tracking system. Once the droppings have been tracked, OpenHAB has an interface
[Eric] has also covered runaway dogs in his design. If Fido passes a geo-fence, OpenHAB will raise the alarm. A handheld dog tracker with its own RFM69 can be used to chase down dogs on the run. Future plans are to miniaturize the dog tracker such that it will be more comfortable for a dog to wear.
Continue reading “Dog Tracker Knows Where The Dirt Is”
How can you not be interested in a project that uses load cells, Bluetooth, a Raspberry Pi, and Twitter. Even for those of our readers without a cat, [Scott’s] tweeting litter box is worth the read.
Each aspect of this project can be re-purposed for almost any application. The inexpensive load cells, which available from eBay and other retailers, is used to sense when a cat is inside the litter box. Typically sensors like the load cell (that contain a strain gauge) this use a Wheatstone bridge, which is very important for maximizing the sensitivity of resistive sensor. The output then goes to a HX711, which is an ADC specifically built for load cells. A simple alternative would be using an instrumentation amplifier and the built-in ADC of the Arduino. Now, the magic happens. The weight reading is transmitted via an HC-06 Bluetooth module to a Raspberry Pi. Using a simple Perl script, the excreted weight, duration, and the cat’s resulting body weight is then tweeted!
Very nice work! This is a well thought out project that we could see being expanded to recognize the difference between multiple cats (or any other animal that goes inside).