Analyzing Hobby Motors with an Oscilloscope

We always like finding new excuses reasons to use our test equipment, so we couldn’t help but be intrigued by this tip from [Joe Mosfet]. He uses the ever-popular Rigol DS1054Z to demonstrate the differences between a handful of brushless motors when rotated by his handheld drill at a constant RPM. Not only is he able to identify a blown motor, but it allows him to visualize their specifications which can otherwise seem a bit mystifying.

One wire from each motor is used as the ground, and channels one and two are connected to the remaining wires. Despite the DS1054Z having four channels, [Joe] is actually only using two of them here. The third channel being displayed is a virtual channel created by a math function on the scope.

After wiring them up, each motor got put into the chuck of his drill and spun up to 1430 RPM. The resulting waveforms were captured, and [Joe] walks us through each one explaining what we’re seeing on the scope.

The bad motor is easy to identify: the phases are out of alignment and in general the output looks erratic. Between the good motors, the higher the Kv rating of the motor, the lower voltage is seen on the scope. That’s because Kv in the context of brushless motors is a measurement of how fast the motor will spin for each volt. The inverse is also true, and [Joe] explains that if he could spin his 2450Kv motor at exactly 2450 RPM, we should see one volt output.

Beyond demonstrating the practical side of Kv ratings, [Joe] also theorizes that the shape of the wave might offer a glimpse into the quality of the motor’s construction. He notes his higher end motors generate a nice clean sine wave, while his cheaper ones show distortion at the peaks. An interesting note, though he does stress he can’t confirm there’s a real-world performance impact.

Last year we featured a similar method for identifying bad brushless motors using a drill press and an oscilloscope, but we liked that [Joe] went through the trouble of testing multiple motors and explaining the differences in their output.

[via /r/multicopter]

MIT Breaks Autonomous Drone Speed Limits By Not Sweating Obstacles

How does one go about programming a drone to fly itself through the real world to a location without crashing into something? This is a tough problem, made even tougher if you’re pushing speeds higher and high. But any article with “MIT” implies the problems being engineered are not trivial.

The folks over at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have put their considerable skill set to work in tackling this problem. And what they’ve come up with is (not surprisingly) quite clever: they’re embracing uncertainty.

Why Is Autonomous Navigation So Hard?

Suppose we task ourselves with building a robot that can insert a key into the ignition switch of a motor vehicle and start the engine, and could do so in roughly the same time-frame that a human could do — let’s say 10 seconds. It may not be an easy robot to create, but we can all agree that it is very doable. With foreknowledge of the coordinate information of the vehicle’s ignition switch relative to our robotic arm, we can place the key in the switch with 100% accuracy. But what if we wanted our robot to succeed in any car with a standard ignition switch?

Now the location of the ignition switch will vary slightly (and not so slightly) for each model of car. That means we’re going to have to deal with this in real time and develop our coordinate system on the fly. This would not be too much of an issue if we could slow down a little. But keeping the process limited to 10 seconds is extremely difficult, perhaps impossible. At some point, the amount of environment information and computation becomes so large that the task becomes digitally unwieldy.

This problem is analogous to autonomous navigation. The environment is always changing, so we need sensors to constantly monitor the state of the drone and its immediate surroundings. If the obstacles become too great, it  creates another problem that lies in computational abilities… there is just too much information to process. The only solution is to slow the drone down. NanoMap is a new modeling method that breaks the artificial speed limit normally imposed with on-the-fly environment mapping.

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Delivery Drones Can Learn From Driving And Cycling

Increasingly these days drones are being used for urban surveillance, delivery, and examining architectural structures. To do this autonomously often involves using “map-localize-plan” techniques wherein first, the location is determined on a map using GPS, and then based on that, control commands are produced.

A neural network that does steering and collision prediction can compliment the map-localize-plan techniques. However, the neural network needs to be trained using video taken from actual flying drones. But generating that training video involves many hours of flying drones at street level putting vehicles and pedestrians at risk. To train their DroNet, Researchers from the University of Zurich and the Universidad Politecnica de Madrid have come up with safer sources for that video, video recorded from driving cars and bicycles.


For the drone steering predictions, they used over 70,000 images and corresponding steering angles from the publically available car driving data from Udacity’s Open Source Self-Driving project. For the collision predictions, they mounted a GoPro camera to the handlebars of a bicycle and drove around a city. Video recording began when the bicycle was distant from an object and stopped when very close to the object. In total, they collected 32,000 images.

To use the trained network, images from the drone’s forward-facing camera were fed into the network and the output was a steering angle and a probability of collision, which was turned into a velocity. The drone remained at a constant height above ground, though it did work well from 1.5 meters to 5 meters up. It successfully navigated road lanes and avoided moving pedestrians and bicycles. Intersections did confuse it though, likely due to the open spaces messing with the collision predictions. But we think that shouldn’t be a problem when paired with map-localize-plan techniques as a direction to move through the intersection would be chosen for it using the location on the map.

As you can see in the video below, it not only does a decent job of flying down lanes but it also flies well in a parking garage and a hallway, even though it wasn’t trained for either of these.

Continue reading “Delivery Drones Can Learn From Driving And Cycling”

3D Printing a Better Quadcopter Frame

Before you smash the “Post Comment” button with the fury of Zeus himself, we’re going to go ahead and say it: if you want to build a decent quadcopter, buy a commercial frame. They are usually one of the cheaper parts of the build, they’re very light for how strong they are, and replacement parts are easily available. While you could argue the cost of PLA/ABS filament is low enough now that printing it would be cheaper than buying, you aren’t going to be able to make a better quadcopter frame on a 3D printer than what’s available on the commercial market.

The frame features a surprisingly low part count.

Having said that, [Paweł Spychalski] has recently shown off his 3D printed FPV racing quadcopter frame with some surprising results. The frame ended up being surprisingly stiff, and while the weight is a bit high, it’s actually lighter than he expected. If you’re looking to build a quad with the absolute minimum of expense his design might be something to look into.

Of course, [Paweł] is hardly the first person to think about printing a quad frame. But he did give his design some extra consideration to try and overcome some of the shortcomings he noticed in existing 3D printed designs. For one, rather than have four separate arms that mount to a central chassis, his design has arms that go all the way across with a thick support that goes between the motors. The central chassis is also reassuringly thick, adding to the overall stiffness of the frame.

The key here is that [Paweł] printed all the parts with 2 mm thick walls. While that naturally equates to longer print times and greater overall weight, it’s probably more than worth it to make sure the frame doesn’t snap in half the first time it touches the ground.

Beyond the printed parts, all you need to assemble this frame are about a dozen M3 nuts and bolts. Overall, between the hardware and the plastic you’re looking at a total cost of under $5 USD. In the video below [Paweł] puts the frame through its paces doing some acrobatic maneuvers, and it looks like 5 bucks well spent to us.

If you want to go all-in on 3D printed quadcopter parts, you can pair this frame with some printed propellers. Perhaps even a printed camera gimbal while you’re at it. Continue reading “3D Printing a Better Quadcopter Frame”

Watching the Watchers: Are You The Star Of an Encrypted Drone Video Stream?

Small aircraft with streaming video cameras are now widely available, for better or worse. Making eyes in the sky so accessible has resulted in interesting footage that would have been prohibitively expensive to capture a few years ago, but this new creative frontier also has a dark side when used to violate privacy. Those who are covering their tracks by encrypting their video transmission should know researchers at Ben-Gurion University of the Negev demonstrated such protection can be breached.

The BGU team proved that a side-channel analysis can be done against behavior common to video compression algorithms, as certain changes in video input would result in detectable bitrate changes to the output stream. By controlling a target’s visual appearance to trigger these changes, a correlating change in bandwidth consumption would reveal the target’s presence in an encrypted video stream.

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Control a Quadcopter over Websockets

The interface

Everyone’s favourite IOT module, the ESP8266, is often the go-to choice for any project that needs quick and cheap control over the web. [Andi23456] wanted to control his quadcopter using the luxury of his mobile phone and thought permanently tethering an ESP12-E module to the quadcopter was exactly what he required.

The ESP8266, really showcasing its all-round prowess, hosts both a web server for a HTML5 based joystick and a Websockets server so that a client, such as a phone, could interact with it over a fast, low latency connection. Once the ESP8266 receives the input, it uses interrupts to generate the corresponding PPM (Pule Position Modulation) code which the RC receiver on the quadcopter can understand. Very cool!

What really makes this realtime(ish) control viable is Websockets, a protocol that basically allows you to flexibly exchange data over an “upgraded” HTTP connection without having to lug around headers each time you communicate. If you haven’t heard of Websockets you really should look really check out this library or even watch this video to see what you can achieve.

Frankendrones: Toy Quads With A Hobby Grade Boost

If you’re not involved in the world of remote controlled vehicles, you may not know there’s a difference between “toy” and “hobby” grade hardware. For those in the RC community, a toy is the kind of thing you’ll find at a big box store: cheap, works OK, but lacking in features and build quality. On the other hand, hobby hardware is generally considered to be of higher quality and performance, as well as being more modular. At the risk of oversimplification: if you bought it ready to go from a store it’s probably a toy, and if you built it from parts it would generally be considered hobby grade.

But with the rock bottom prices of toy quadcopters, that line in the sand is having a harder time than ever holding some in the community back. The mashup of toy and hobby grade components is giving rise to the concept of “frankendrones” that combine the low cost of toy hardware with key upgrades from the hobby realm. Quadcopter blogger [garagedrone] has posted a roundup of modifications made to the Bayangtoys X16, a $99 quadcopter which is becoming popular in the scene.

Some of the modifications are easy enough for anyone to do. Swapping out the original propellers for ones meant for the DJI Phantom 3 increases performance and doesn’t even require tools. If you want to go a bit further down the rabbit hole, you can cut off the X16’s battery connector and replace it with a standard XT60. That lets you use standard 3S LiPo batteries, which are cheaper and higher capacity than the proprietary ones the toy shipped with.

If you have a 3D printer, there are also a number of upgraded parts you can print which will bolt right onto the X16. Payload adapters, landing gear, and GoPro mounts are all just a few clicks (and some filament) away. This library of 3D printable parts is made possible in part because the X16’s frame is itself a clone of another toy quadcopter, the popular Syma X8C. So anything listed as compatible with the Syma X8C should work with the X16 (and vice versa).

Finally, if you really want to take the X16 to the next level, you can swap out the flight controller with an open source and better supported hobby grade model. Some of these flight controllers and associated new receivers can end up costing about half as much as the X16 did to begin with, but the vast improvement in performance and capability should more than make up for the cost.

We’ve covered previous efforts to increase the performance of low cost quadcopters in the past, as well as builds that put frugality front and center. It seems that no matter what your budget is a screaming angel of death is available if you want it.

Thanks to [Calvin] for the tip.

Continue reading “Frankendrones: Toy Quads With A Hobby Grade Boost”