Wearables and robots don’t often intersect, because most robots rely on rigid bodies and programming while we don’t. Exoskeletons are an instance where robots interact with our bodies, and a soft exosuit is even closer to our physiology. Machine learning is closer to our minds than a simple state machine. The combination of machine learning software and a soft exosuit is a match made in heaven for the Harvard Biodesign Lab and Agile Robotics Lab.
Machine learning studies a walker’s steady gait for twenty periods while vitals are monitored to assess how much energy is being expended. After watching, the taught machine assists instead of assessing. This type of personalization has been done in the past, but the addition of machine learning shows that the necessary customization can be programmed into each machine without a team of humans.
Exoskeletons are no stranger to these pages, our 2017 Hackaday Prize gave $1000 to an open-source set of robotic legs and reported on an exoskeleton to keep seniors safe.
Continue reading “Learning Software In A Soft Exosuit”
We were excited to learn that someone had started working with force sensors on filament extruders, especially after we posted about a recent development in filament thickness sensors.
[airtripper] primarily uses a Bowden extruder, and wanted to be a little more scientific in his 3D printing efforts. So he purchased a force sensor off eBay and modified his extruder design to fit it. Once installed he could see exactly how different temperatures, retraction rates, speed, etc. resulted in different forces on the extruder. He used this information to tune his printer just a bit better.
More interesting, [airtripper] used his new sensor to validate the powers of various extruder gears. These are the gears that actually transfer the driving force of the stepper to the filament itself. He tested some of the common drive gears, and proved that the Mk8 gear slipped the least and provided the most constant force. We love to see this kind of science in the 3D printing community — let’s see if someone can replicate his findings.