Recharging Drones On The Go With A Supercharger

April 10, 2018 by Brian Benchoff 45 Comments

If Techcrunch is to be believed, our skies will soon be filled with delivery robots, ferrying tacos and Chinese food and Amazon purchases from neighborhood-area dispatch stations to your front door. All of this is predicated on the ability of quadcopters to rapidly recharge their batteries, or at the very least swap out batteries automatically.

For their Hackaday Prize entry, [frasanz], [ferminduaso], and [david canas] are building the infrastructure that will make delivery drones possible. It’s a drone supercharger, or a robot that grabs a drone, swaps out the battery, and sends it off to deliver whatever is in its cargo compartment.

This build is a droneport of sorts, designed to have a drone land on it, have a few stepper motors and movable arms spring into action, and replace the battery with a quick-change mechanism. This can be significantly more difficult than it sounds — you need to grab the drone and replace the battery, something that’s easy for human eyes and hands, but much harder for a few sensors and aluminum extrusion.

To change batteries, the team is just letting the drone land somewhere on a platform that’s a few feet square. Arms then move it, pushing the drone to the center, and a second arm then moves in to swap the battery. The team is using an interesting locking cam solution to clamp the battery to the drone. It’s much easier for a machine to connect than the standard XT-60 connector found on race quads.

Is this the project the world needs? Quite possibly so. Drones are going to be awesome once battery life improves. Until then, we’ll have to live with limited flight times and drone superchargers.

Continue reading “Recharging Drones On The Go With A Supercharger” →

Watch This Tiny Dome Auto-open And Close Into A Propeller

March 30, 2018 by Donald Papp 16 Comments

Careful planning and simulation is invaluable, but it can also be rewarding to dive directly into prototyping. This is the approach [Carl Bugeja] took with his Spherical Folding Propeller design which he has entered into the Open Hardware Design Challenge category of The 2018 Hackaday Prize. While at rest, the folding propeller looks like a small dome attached to the top of a motor. As the motor fires up, centrifugal forces cause the two main halves of the dome to unfold outward where they act as propeller blades. When the motor stops, the assembly snaps shut again.

[Carl] has done some initial tests with his first prototype attached to a digital scale as a way of measuring thrust. The test unit isn’t large — the dome is only 1.6 cm in diameter when folded — but he feels the results are promising considering the small size of the props and the fact that no simulation work was done during the initial design. [Carl] is looking to optimize the actual thrust that can be delivered, now that it has been shown that his idea of a folding dome works as imagined.

Going straight to physical prototyping with an idea can be a valid approach to early development, especially nowadays when high quality components and technologies are easily available even to hobbyists. Plus it can be great fun! You can see and hear [Carl]’s prototype in the short video embedded below.

Continue reading “Watch This Tiny Dome Auto-open And Close Into A Propeller” →

Control A Swarm Of RC Vehicles With ESP8266

March 17, 2018 by Tom Nardi 9 Comments

Over at RCgroups, user [Cesco] has shared a very interesting project which uses the ever-popular ESP8266 as both a transmitter and receiver for RC vehicles. Interestingly, this code makes use of the ESP-Now protocol, which allows devices to create a mesh network without the overhead of full-blown WiFi. According to the Espressif documentation, this mode is akin to the low-power 2.4GHz communication used in wireless mice and keyboards, and is designed specifically for persistent, peer-to-peer connectivity.

Switching an ESP8266 between being a transmitter or receiver is as easy as commenting out a line in the source code and reflashing the firmware. One transmitter (referred to as the server in the source code) can command eight receiving ESP8266s simultaneously. [Cesco] specifically uses the example of long-range aircraft flying in formation; only coming out of the mesh network when it’s time to manually land each one.

[Cesco] has done experiments using both land and air vehicles. He shows off a very hefty looking tracked rover, as well as a quickly knocked together quadcopter. He warns the quadcopter flies like “a wet sponge”, but it does indeed fly with the ESP’s handling all the over the air communication.

To be clear, you still need a traditional PPM-compatible RC receiver and transmitter pair to use his code. The ESPs are simply handling the over-the-air communication. They aren’t directly responsible for taking user input or running the speed controls, for example.

This isn’t the first time we’ve seen an ESP8266 take the co-pilot’s seat in a quadcopter, but the maniacal excitement we feel when considering the possibility of having our very own swarm of flying robots gives this particular project an interesting twist.

MIT Breaks Autonomous Drone Speed Limits By Not Sweating Obstacles

February 12, 2018 by Will Sweatman 29 Comments

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.

Continue reading “MIT Breaks Autonomous Drone Speed Limits By Not Sweating Obstacles” →

Delivery Drones Can Learn From Driving And Cycling

February 6, 2018 by Steven Dufresne 10 Comments

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 Printed Propellers Take To The Skies

January 6, 2018 by Lewin Day 10 Comments

In the world of drones, propeller choice is key to performance. Selecting the right props can have a major effect on things like flight time, vibration, and a whole host of other factors. Thinking it might be fun to experiment, [RCLifeOn] decided to 3D print some props and head out for a fligh t.

The props are a fairly simple 3-bladed design, which were printed in both PETG and PLA. No major difference is noted between the two materials, and the quadcopter under test is able to fly with either. It was noted that the props perform particularly poorly in a crash, with all props failing even in the softest of crashes. We would recommend some eye (and body) protection when spinning these props up for the first time.

If you’re keen to try them out yourself, the STL file can be had here. The video notes that when printing 4 props, 2 must be reversed in the Y-axis to print a counter-rotating set of 4. The instructions used for creating propellers in Fusion3D are available here.

It’s a worthy experiment, and something we’d like to see more of. With a 3D printer, it’s possible to experiment with all manner of propeller designs, and we’d love to see the best and worst designs that are still capable of flight. We’ve also seen 3D printed props before, like this effort from [Anton].

The Zombie Rises Again: Drone Registration Is Back

December 14, 2017 by Jenny List 69 Comments

It’s a trope of horror movies that demonic foes always return. No sooner has the bad guy been dissolved in a withering hail of holy water in the denoeument of the first movie, than some foolish child in a white dress at the start of the next is queuing up to re-animate it with a careless drop of blood or something. If parents in later installments of popular movie franchises would only keep an eye on their darn kids, it would save everybody a whole lot of time!

The relevant passage can be found in section 1092(d) of the National Defense Authorization Act, on page 329 of the mammoth PDF containing the full text, and reads as follows:

(d) RESTORATION OF RULES FOR REGISTRATION AND MARKING OF UNMANNED AIRCRAFT
.—The rules adopted by the Administrator
of the Federal Aviation Administration in the matter of registration
and marking requirements for small unmanned aircraft (FAA-2015-
7396; published on December 16, 2015) that were vacated by the
United States Court of Appeals for the District of Columbia Circuit
in Taylor v. Huerta (No. 15-1495; decided on May 19, 2017) shall
be restored to effect on the date of enactment of this Act.

This appears to reverse the earlier decision of the court, but does not specify whether there has been any modification to the requirements to prevent their being struck down once more by the same angle of attack. In particular, it doesn’t change any of the language in the FAA Modernization Act of 2012, which specifically prevents the Agency from regulating hobby model aircraft, and was the basis of Taylor v. Huerta. Maybe they are just hoping that hobby flyers get fatigued?

We took a look at the registration system before it was struck down, and found its rules to be unusually simple to understand when compared to other aviation rulings, even if it seemed to have little basis in empirical evidence. It bears a resemblance to similar measures in other parts of the world, with its 250 g weight limit for unregistered machines. It will be interesting both from a legal standpoint to see whether any fresh challenges to this zombie law emerge in the courts, and from a technical standpoint to see what advances emerge from Shenzhen as the manufacturers pour all their expertise into a 250 g class of aircraft.

Thanks [ArduinoEnigma] for the tip.