No matter what they’re flying, good pilots have a “feel” for their aircraft. They know instantly when something is wrong, whether by hearing a strange sound or a feeling a telltale vibration. Developing this sixth sense is sometimes critical to the goal of keeping the number of takeoff equal to the number of landings.

The same thing goes for non-traditional aircraft, like paragliders, where the penalty for failure is just as high. Staying out of trouble aloft is the idea behind this paraglider line tension monitor designed by pilot [Andre Bandarra]. Paragliders, along with their powered cousins paramotors, look somewhat like parachutes but are actually best described as an inflatable wing. The wing maintains its shape by being pressurized by air coming through openings in the leading edge. If the pilot doesn’t maintain the correct angle of attack, the wing can depressurize and collapse, with sometimes dire results.

Luckily, most pilots eventually develop a feel for collapse, sensed through changes in the tension of the lines connecting the wing to his or her harness. [Andre]’s “Tensy” — with the obligatory “McTenseface” surname — that’s featured in the video below uses an array of strain gauges to watch to the telltale release of tension in the lines for the leading edge of the wing, sounding an audible alarm. As a bonus, Tensy captures line tension data from across the wing, which can be used to monitor the performance of both the aircraft and the pilot.

There are a lot of great design elements here, but for our money, we found the lightweight homebrew strain gauges to be the real gem of this design. This isn’t the first time [Andre] has flown onto these pages, either — his giant RC paraglider was a big hit back in January.

Thanks to [mip] for the tip.

We all know how important it is to achieve balance in life, or at least so the self-help industry tells us. How exactly to achieve balance is generally left as an exercise to the individual, however, with varying results. But what about our machines? Will there come a day when artificial intelligences and their robotic bodies become so stressed that they too will search for an elusive and ill-defined sense of balance?

We kid, but only a little; who knows what the future field of machine psychology will discover? Until then, this kinetic sculpture that achieves literal balance might hold lessons for human and machine alike. Dubbed In Medio Stat Virtus, or “In the middle stands virtue,” [Astrid Kraniger]’s kinetic sculpture explores how a simple system can find a stable equilibrium with machine learning. The task seems easy: keep a ball centered on a track suspended by two cables. The length of the cables is varied by stepper motors, while the position of the ball is detected by the difference in weight between the two cables using load cells scavenged from luggage scales. The motors raise and lower each side to even out the forces on each, eventually achieving balance.

The twist here is that rather than a simple PID loop or another control algorithm, [Astrid] chose to apply machine learning to the problem using the Q-Behave library. The system detects when the difference between the two weights is decreasing and “rewards” the algorithm so that it learns what is required of it. The result is a system that gently settles into equilibrium. Check out the video below; it’s strangely soothing.

We’ve seen self-balancing systems before, from ball-balancing Stewart platforms to Segway-like two-wheel balancers. One wonders if machine learning could be applied to these systems as well.

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).