Hybrid Robot Walks, Transforms, And Takes Flight

[Project Malaikat] is a 3D printed hybrid bipedal walker and quadcopter robot, but there’s much more to it than just sticking some props and a flight controller to a biped and calling it a day. Not only is it a custom design capable of a careful but deliberate two-legged gait, but the props are tucked away and deployed on command via some impressive-looking linkages that allow it to transform from walking mode to flying mode.

Creator [tang woonthai] has the 3D models available for download (.rar file) and the video descriptions on YouTube contain a bill of materials, but beyond that there doesn’t seem to be much other information available about [Malaikat]. The creator does urge care to be taken should anyone use the design, because while the robot may be small, it does essentially have spinning blades for hands.

Embedded below are videos that show off the robot’s moves, as well as a short flight test demonstrating that while control was somewhat lacking during the test, the robot is definitely more than capable of actual flight.

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Learning Software In A Soft Exosuit

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.

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Hackaday Prize Entry: Two Leg Robot

If you’re working on your own bipedal robot, you don’t have to start from the ground up anymore. [Ted Huntington]’s Two Leg Robot project aims to be an Open Source platform that’ll give any future humanoid-robot builders a leg up.

While we’ve seen quite a few small two-legged walkers, making a pair of legs for something human-sized is a totally different endeavor. [Ted]’s legs are chock-full of sensors, and there’s a lot of software that processes all of the data. That’s full kinematics and sensor info going back and forth from 3D model to hardware. Very cool. And to top it all off, “Two Leg” uses affordable motors and gearing. This is a full-sized bipedal robot platform that you might someday be to afford!

Will walking robots really change the world? Maybe. Will easily available designs for an affordable bipedal platform give hackers of the future a good base to stand on? We hope so! And that’s why this is a great entry for the Hackaday Prize.

Measuring Walking Speed Wirelessly

There are a lot of ways to try to mathematically quantify how healthy a person is. Things like resting pulse rate, blood pressure, and blood oxygenation are all quite simple to measure and can be used to predict various clinical outcomes. However, one you may not have considered is gait velocity, or the speed at which a person walks. It turns out gait velocity is a viable way to predict the onset of a wide variety of conditions, such as congestive heart failure or chronic obtrusive pulmonary disease. It turns out, as people become sick, elderly or infirm, they tend to walk slower – just like the little riflemen in your favourite RTS when their healthbar’s way in the red. But how does one measure this? MIT’s CSAIL has stepped up, with a way to measure walking speed completely wirelessly.

You can read the paper here (PDF). The WiGate device sends out a low-power radio signal, and then measures the reflections to determine a person’s location over time. Alone, however, this is not enough – it’s important to measure the walking speed specifically, to avoid false positives being triggered by a person simply not moving while watching television, for example. Algorithms are used to separate walking activity from the data set, allowing the device to sit in the background, recording walking speed data with no user interaction required whatsoever.

This form of passive monitoring could have great applications in nursing homes, where staff often have a huge number of patients to monitor. It would allow the collection of clinically relevant data without the need for any human intervention; the device could simply alert staff when a patient’s walking pattern is indicative of a bigger problem.

We see some great health research here at Hackaday – like this open source ECG. Video after the break.

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Train Your Robot To Walk with a Neural Network

[Basti] was playing around with Artificial Neural Networks (ANNs), and decided that a lot of the “hello world” type programs just weren’t zingy enough to instill his love for the networks in others. So he juiced it up a little bit by applying a reasonably simple ANN to teach a four-legged robot to walk (in German, translated here).

While we think it’s awesome that postal systems the world over have been machine sorting mail based on similar algorithms for years now, watching a squirming quartet of servos come to forward-moving consensus is more viscerally inspiring. Job well done! Check out the video embedded below.

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Floating Walking Robot

It’s no secret that we love bizarre robot locomotion, so we are naturally suckers for BALLU (YouTube link, also embedded below) the Bouyancy-Assisted Lightweight Legged Unit. The project started with a simple observation — walking robots are constrained by having to hold themselves up — and removing that constraint make success much easier. Instead of walking, BALLU almost floats and uses what little net weight it does have to push against the ground.

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Vote With Your Feet

Gamifying life is silly, fun, and a great way to interact with those strangers who you pass everyday. Here’s one example that might just pop up along your next walk to work. It’s a way to take a very unscientific straw poll on any topic — you won’t even have to use your hands to cast a ballot.

A group called [Vote With Your Feet] has come up with a novel way of casting ballots. Simply walk down the sidewalk and through one of two doorways, each labeled with either side of a dichotomy. Each doorway is able to count the number of people that pass through it, so any issue imaginable can be polled. They already did vim vs emacs (59 to 27),  and we’d like to see Keynes vs Hayek, or even Ovaltine vs Nesquik. Users can send the machine new issues for the masses to vote on, so the entertainment is quite literally limited only by your imagination.

thumbThe physical build is well documented. Since this is used outside, the choice of a flipdot display (of course always fun to play with) is perfect for this high-contrast in any level of light. Each doorway has a break-beam sensor which is monitored by the Raspberry Pi driving the overhead display (here’s code for it all if you want to dig in).

The point of this art installation like this is to get people to interact with their environment in a novel way, which this project has accomplished exceptionally well. In 3 days, they registered over 10,000 votes which are viewable on their website. If you have a project in mind that calls for data visualization you might want to keep this in your back pocket.

We have also seen other ways that doorways can count people outside of voting, if you’re looking for any inspiration yourself.