Category: Crazyflie

We’ve had an exciting year in 2021, and we’re eager to see what 2022 will bring ! Let’s see what’s in the pipeline and what we hope for this new year.

Products

The AI deck and Bolt out of Early Access

We’ve put a lot of efforts during these last months on working with the AI deck’s firmware and infrastructure. With great help of our intern Rik, we managed to make huge leaps, and hopefully sometimes in the coming months we’ll be able to share what we worked on. I can already tell you that the incoming release will bring some needed improvements on flashing on the GAP8 chip and improved image streaming! As the AI deck is one of the most challenging of our decks, we also hope to add an extensive tutorial (that we call the “mega tutorial”) to help you working with it.

Also we have started to push some framework changes to make it easier for you to make bigger drones with the Crazyflie Bolt. One of those are the persistent parameter system that we have recently implemented on the Crazyflie’s, so we will add more and more of these types of features. The hope is, is that we are able to provide some kind of assembly kit for a larger Bolt-based drone, of which we already did some initial battery investigations for.

Prototypes

Fun Fridays are usually our time to play around with new possibilities and prototypes. Marcus has already made great strides, and hopefully in 2022 we’ll be able to go even farther with those. Arnaud has also been working on the much waited new iteration of the Crazyradio, with a new chip and an improved communication protocol. Tobias, our dedicated hardware man, has also ideas down the pipes in the form of a brushless Crazyflie as we already showed in our future plans presentation of November’s BAMdays. Also we hope to initiate the design process of a new and improved version of the Crazyflie with more power and processing capabilities.

People and Collaborations

Last year we have continued our close collaboration with researchers at institutes and universities, to help them out with achieving their goals and contribute their work to our opensource firmware and software. It proves really fruitful, both for us and the people we talk to, so we hope 2022 will see yet again closer and newer connections.

We were really happy with our first own online conference, which helped us reconnect and talk to our community about all the awesomeness achieved with the Crazyflies. We hope to implement something similar on a more regular basis, to keep talking about collaborations, possibilities, and in general sharing all the work that’s been done on the platform. Those “lightweight” BAM should arrive soon, so keep updated if you want to join them!

Component shortage and productions issues

We expect to still deal with the component shortage, as it is expected to last for at least another year, even two. Production is therefore a continuous challenge, with a lot of unpredictability, and we will find better solutions to deal with it in 2022. Thankfully, we have good hopes on keeping good stock levels throughout the crisis, as we’ve increased our stock. We’ll of course keep you updated on any big updates regarding the crisis and how it is affecting it us.

Unfortunately, the component shortage also means that it’s harder to make prototypes. It’s difficult to find and/or buy just one chip, so it causes delays in our creative hardware developments. It is what it is… but we will sure be able to find solutions – as we did during our 10 years’ history!

Anything else?

Of course our heads are always full of ideas and we are passionate to work on anything! We have ambitions in developing a simulation for our users or CI, doing more measurements with the new thrust stand or adding further improvements to our documentation and tutorials. And we might also meet new interesting people (digitally or in person?) who might give us enough inspiration to start something completely new! Soon we will have our quarterly meeting, where we try to herd and select our passions and ideas into conceivable plans and actions.

With all these exciting projects, we’re really excited to see what 2022 has in store for us! I hope you too have an awesome year 2022.

2021 is coming to an end. As we’re about to flip the page on a new calendar, let’s take a look at what happened during this past year.

Community

It’s no news to you, but keeping in touch with our community during a pandemic has forced us to try new ways to meet and interact. The main event for us this year has been the BAM days: our first conference held entirely by ourselves, full of exciting talks and fun, and we’re very happy with how it went down! You can still watch the talks we hosted during those three days in our dedicated Youtube playlist.

Guest blogposts

Once again, we’ve had the honor to host some awesome guests on our blog, which you can read (or re-read) here:

Software

We had 3 releases this year, (one in January, one in March and one in June). We worked a lot on improving things like the Crazyflie logging and parameter interface or wondered how to deal with our API. We’ve also implemented a new way to store things, and we now have new, powerful persistent parameters.

Thanks to Jonas’ hardwork, we also set up a “crazy lab” here in Malmö. During Fun Friday, Arnaud has been experimenting with Rust, working on a webclient.

Hardware

We’ve been dealing since 2020 with a hardware crisis. The component shortage has made production erratic and difficult, and for the first time in Bitcraze’s history, we’ve had to increase our prices.

Lighthouse

The first part of the year was dedicated to the Lighthouse system. At the beginning of the year, we finally got it out of early access. We documented the new Lighthouse Functionality and even wrote a paper for ICRA about the Lighthouse accuracy with Wolfgang’s help. We created the Lighthouse swarm bundle, getting every element to fly a swarm with this positioning system.

AI deck

We worked a lot on the AI deck this year. We upgraded to the AI deck 1.1, with a gray-scale camera and a newer version of the GAP-8. Part of this work was also to improve the documention and informations we had on it. We had a workshop with PULP, and dreamed of a mega-tutorial.

Documentation

As usual, we’ve been trying to improve our documentation. This year, it included an API reference in our Python library , and rethinking our structure

Bitcraze

Bitcraze has once again increased in numbers, as we welcomed into our ranks Jonas. We were also really happy to add Wolfgang to the team for a few months, as well as Rik, an intern from MAV-lab.

The biggest event this year for us was our 10 year anniversary. That’s right, Bitcraze turned 10 in September, and we tried to celebrate it as much as possible ! With a nice outing and with the BAM days, but also with a video trying to show what 10 years of Bitcraze looks like:

Christmas is just around the corner, and it’s time for the traditionnal Christmas video! This year, we wanted to use the AI deck as we’ve been working hard on this deck for some time now. Showcasing its new feature in this festive video seemed the best idea.

Santa this year needed help to find and get the presents delivered, so he asked for help from Bitcraze! Let’s see how it played out:

I’m sure you’re wondering how we managed to set this up, so let’s discuss how we did it!

Picking up the packages

The goal was to pick up some packages and place them in the sleigh, and it all worked out pretty well. All the flying in the video is scripted using the python lib and positioning is done using Qualisys’ motion capture system with Active marker decks. The trajectories are hard coded and with some careful adjustments we managed to lift and fly the 4 crazyflies attached to the same present, even though it was a bit wobbly from time to time. Getting the present into the sleigh was not as easy, and we might have taken some short cuts here as well as when attaching/removing lines.

Sometimes detangling the wires was a teamwork

Picking up the second package needed some precision, and it went incredibly well, on the first try! The present was very light and needed someone to hold it, to prevent it from moving when the Crazyflie approached.

Getting the sleigh off

Our first tries included 5 strings attached to the sleigh, but it was difficult to get the right tension at the right time to have the UAVs actually pull the weight. Here came the “rubber band solution”: we just attached all the strings to a rubber band, that was itself attached to the sleight. That way, the tension could get even when all the Crazyflies were in the air and ready to pull the sleight.

The rubber band

The AI deck camera/streamer

When we started this project, the intention was to run a neural network in the AI-deck to identify or classify the presents. We did not manage to get to a point where we had something that actually added value to the story, so we settled for just streaming the video from the AI-deck camera on the scouting drone instead.

The AI-deck example with color camera viewer is still under development, but if you want to give it a try you can take a look at the readme in the github repo.

Bloopers

And finally, as an added bonus, if you ever wonder how many tries it takes to make 5 Crazyflies pull a sleigh, here is a little behind-the-scenes video too!

I have returned from my family visit in California, who I’ve haven’t seen them in 3 years due to Covid. To spend the most possible time with them, the plan was that I would still work full time for Bitcraze from my father’s home. The problem became however, that it wouldn’t fit so well in our current way of work as I would miss all the morning stand up meetings due to the large time difference between Sweden and California (-9 hours). That is why we settled that I would work on separate projects/investigations during my time away. So I thought it would be a great opportunity to dig into ROS and 3D simulations again and see what the latest state of that is! So about the simulations is what I’ll be mostly talking about right in this blog post, in terms of what simulators are out there and what simulation development is currently ongoing.

Need for simulation?

Why would it be actually be necessary to have a simulation in our current frame work? Just to give an example, my new colleague Jonas recently tried out his hand on the CFlib swarm class for the first time for the BAMdays tutorials, and simulator would have been great during that initial porcess. Namely, most of the crashes were not necessary due to low batteries or bad communication, but mostly due to the fact that he was not able to double check his script beforehand. If one is able to check if all the programmed positions of the Crazyflies are implemented as they should before an actual flight, this would prevented a lot of broken propellers!

Just to note here that there are a lot of types of simulations that you can think of. Earlier this year had our ex-interns Max and Josephine finish an Renode simulation of the Crazyflie’s microcontrollers. We’ve also seen the word Simulink pop-up multiple times on the forum which indicates that quite some control classes are investigating the dynamic model of the Crazyflie. However, the type of simulation that I’m currently referring to are the 3D simulators in which a robot or quadcopter can move and interact with a virtual environment, with usually an physics engine in effect.

Crazyflie in Gazebo (+ROS)

During some initial investigation there were already some simulations that pop out. First of all I went and looked into what is available for Gazebo at the moment, which is:

CrazyS is based on the RotorS simulation with some additional off-board crazyflie controllers for position control. I wasn’t able to build it for my Ubuntu 20.04 just yet myself, but that there is ongoing work to port CrazyS to ROS Noetic. For now on a virtual machine with ROS melodic it build just fine! Note my laptop did had to work quite hard when I wanted to simulate more than 1 Crazyflie, but the physics and plugins that were made for Gazebo is enabling many to do a lot for their research. Please check out the core papers about CrazyS!

Sim_cf is perhaps a little lesser known, but the project does stand out as it has some interesting features to it. It is for instance, possible to use the actual c-based firmware in software-in-the-loop (SITL) mode, which controls the simulated Crazyflie. It is even possible to use an actual crazyflie with an hardware-in-the-loop (HITL) simulation. Eventhough the project is not actively maintained anymore, I did manage to build it from source for ROS Noetic and Gazebo 11, although I was not able to fly more than 4 do to errors.

Other Simulators

Ofcourse Gazebo is not the only possibility out there. I also had a quick go at another simulator called Webots, which is quite an interesting option indeed as well. Currently there is only one quadcopter model available, so it only makes sense for it to also contain an Crazyflie! They do use their own robotic format, so probably the easiest process would be, is to convert an existing model for Gazebo/ROS into an format that Webots can understand.

Also, quite recently, a trending tweet has brought us to the attention of a Rviz based Crazyflie simulation! This looks quite promising as well, so I will try this out quite soon too.

Screenshot from 2021-11-15 11-56-48
Crazyflie in Ignition Gazebo

Ongoing work in Ignition Gazebo

So in the future, the current Gazebo in its form will disappear and will be only be part of Ignition. So that is why it made sense for me to start playing with an separate Crazyflie model and plugins for the Ignition frame work instead. Moreover, it seems that quite some elements and plugins based on the RotorS simulation for the original gazebo, are now fully integrated within the Ignition gazebo framework, which should make it more easier to make quadcopter models fly. Currently it’s still work in progress, so right now is only to be found on my personal github repository, but as soon as it becomes more fleshed out and stable, this will probably transferred to Bitcraze’s github repos and we will write a more elaborate blogpost about it. For now, I’ll try to work on it further as my Fun Friday project!

In the mean time, we have started a simulation discussion thread in the Crazyswarm2 repository, which is an ongoing port of Crazyswarm to ROS2. It would be the ideal situation if we would be able to use this simulator for both Crazyswarm and our native CFlib! But I’ve mostly have used Gazebo in the past, so if there are any other simulators that we should try out too, please join the discussion and let us know!

The Crazyflie parameter framework provides a way to get, set and monitor parameters in the quadcopter. Examples of a parameter include which effect the LED ring will display as well as which controller to use for flight.

The parameters have default values and up until now have been reset to those default values on each restart of the Crazyflie. This has not been seen as much of a problem (Or has it? Let us know!) since most use of the Crazyflie platform has been session based and the need for persistent parameters has been low. Our work with getting the Crazyflie Bolt out of early access has however changed this need.

The Crazyflie Bolt kit

The Bolt will need different values for the tuning parameters for controllers and being forced to set these on each boot would be pretty annoying. And since we want the Crazyflie Bolt and the Crazyflie 2.1 to share the same firmware, persistent parameters would come in handy.

Fortunately the Crazyflie includes an EEPROM (Electronically Erasable Programmable Read-Only Memory) which we can use to store parameter values and have them survive restarts of the quad.

So this means, with the latest branches of the firmware and the python library, you can set values of a persistent parameter, store it to EEPROM, and the parameter will keep its values across reset and even across firmware upgrades.

Persistent Parameter API

Not all parameters will be able to be persistent and stored in the EEPROM. We have added a way to declare a parameter as persistent in the firmware:

PARAM_ADD_CORE(PARAM_UINT8 | PARAM_PERSISTENT, effect, &effect)

This will allow the new API added to our python library to be used with this parameter. The API consists of three actions:

  • Getting the state of a persistent parameter
  • Storing the current value of a persistent parameter
  • Clearing the stored value of a persistent parameter

The state of a persistent parameter consist of three pieces of information:

  • Is the value stored or not
  • The default value of the parameter
  • The stored value of the parameter

Setting a value, using the regular parameter API, is not enough to store the value to the EEPROM for a persistent parameter, you need to be explicit. A special API call is needed to store the current value to memory. And to stop storing the value you need to use the clear API action.

Python API

The above choices corresponds to three methods in our Python API:


def persistent_get_state(self, complete_name, callback)

Get the state of the specified persistent parameter. The state will be returned in the supplied callback. The state is represented as a named tuple with members: is_storeddefault_value and stored_value. The state is None if the parameter is not persistent or if something goes wrong.


def persistent_store(self, complete_name, callback=None)

Store the current value of the specified persistent parameter to EEPROM. The supplied callback will be called with True as an argument on success, and with False as an argument on failure.


def persistent_clear(self, complete_name, callback=None)

Clear the current value of the specified persistent parameter from EEPROM. The supplied callback will be called with True as an argument on success and with False as an argument on failure.


Example code

We have also added an example script that will showcase the functionality, it will list the state of all persisted parameters in the firmware, and will store and clear some of them. The output when run against current master is:

$ CFLIB_URI=radio://0/80/2M/E7E7E7E7E8 python3 examples/parameters/persistent_params.py 
-- All persistent parameters --
sound.effect: PersistentParamState(is_stored=False, default_value=0, stored_value=None)
ring.effect: PersistentParamState(is_stored=True, default_value=6, stored_value=2)

Set parameter ring.effect to 10

Store the new value in persistent memory
Persisted ring.effect!
The new state is:
ring.effect: PersistentParamState(is_stored=True, default_value=6, stored_value=10)

Clear the persisted parameter
Cleared ring.effect!
The new state is:
ring.effect: PersistentParamState(is_stored=False, default_value=6, stored_value=None)

You can count on more parameters to be marked as persistent in the near future. Hopefully this will be useful for you! Please report any issues you find!

Happy Hacking!

The semiconductor/electrical components shortage has not gone unnoticed by now, especially with the news coverage it had so far. It’s effecting most industries dealing with electrical components such as car manufacturers, which seem to have an especially hard time. We have been doing our best to handle the crisis, often solving it by throwing money at the problem. When you are a small player like us, you just don’t get prioritized by the big electronics manufacturers, and paying more at the open market is most often the only solution. In the beginning of May this year we had to increase our prices to counteract our increased cost as communicated in this blog post. Hopefully we will not have to increase them more but the shortage is now estimated to last for yet another year or two, and who knows how it eventually will effect us.

Another outcome of the semiconductor shortage is that the manufacturing lead-times has become very long and uncertain, so stock management has become much harder. We have solved it partially by increasing our stock but that also requires capital, which is a limited resource. We think we have managed quite OK so far but as of now Crazyflie 2.1, Multiranger-deck and SD-card decks are out of stock. Crazyflie 2.1 will hopefully arrive at the end of this week and the Multiranger and SD-card decks at the end of December. Even though times are a bit tough right now we are positive and believe we will be able to keep good stock levels throughout the crisis. We might not be able to guarantee that no products will be out of stock, but we can promise to do our very, very best to stay on top of this situation!

Sometimes we get the question of where to modify or add code to change some behavior of the Crazyflie. There is no quick answer to this question but we thought that we should write a post to clear up some question marks and give a better idea of how to approach the problem.

There are quite a few repositories on the Bitcraze github page but there are two that are the main focal point for almost any Crazyflie work, those are the crazyflie-firmware and the crazyflie-lib-python. The crazyflie-firmware contains the source code (written in C) for the firmware that runs in the Crazyflie, that is the code responsible for flying, blinking LEDs, communicating with the radio, scanning sensors and so on. The crazyflie-lib-python (often called the python lib) on the other hand is running on the PC side and is the API to use to communicate with the Crazyflie from a script. The crazyflie-lib-python is also used by the python client which means that anything you see in the client can be done by a script using the python lib.

Let’s assume we have a system of one Crazyflie connected to a computer using a Crazyradio. Now we want to control the Crazyflie and make it take off for instance, how should this be done?

The easiest way would be to use the python lib. The python lib is used to communicate with the Crazyflie and we can use it to send instructions to the Crazyflie, for instance to take off or fly a trajectory. It is also possible to use the parameter framework to change values in the Crazyflie. The main way of monitoring what is going on in the Crazyflie is to use the log framework to read variables from the Crazyflie. The python lib is perfect for controlling the Crazyflie or prototype ideas as it is very fast to make changes and try things out. The best to get started with the python lib is to start from an example that already uses functionalities you want to use.

Another option is to add code in the firmware. Originally this has been quite hard since the firmware has not been initially designed to accept user code. This means that unless you want to modify an already existing code, it is quite hard to find where to add your code so that it runs in the Crazyflie firmware and you would have to make a fork of the firmware which can he hard to maintain and keep up to date in the long run. This is one of the things the out-of-tree build and the app layer is solving, it is now quite easy to add and run your own C files to the firmware in your own project without having to fork the Crazyflie firmware. There is a bunch of examples in the firmware that shows how to implement autonomous behavior as an app. The easiest is to start with the hello world example. When it comes to modifying exiting functionality in the firmware code, most of the time forking and modifying the official firmware unfortunately is the only solution. We are however working our way to make more and more of the firmware modular so that it can be expanded out of tree. For example there has been work to make an out of tree estimator possible to implement.

A prototype written as a python script is often pretty easy to move to the Crazyflie firmware. This is a good pattern when writing an application, rapid prototyping in python and then finalizing in firmware if needed. The best example of that is the push demo. It is a demo where the Crazyflie can be pushed-around with the help of the flow deck for autonomous flight and the multiranger deck for detecting obstacle/hands. We have a python cflib push demo as well as a Crazyflie firmware push demo app.

There is some support in the python lib for interacting with multiple Crazyflies and it is probably a good start point for simple swarms. For more advanced swarm work Crazyswarm may be a better option.

If you would like to see some of the process in action, we have made a workshop during our BAM days about implementing functionality both using the python lib and in the firmware as an out of tree app:

During the preparations for BAM, for example the awesome demo that Kristoffer did (link to demo), we had a lot of discussions about landing on charging pads. The Qi deck is a good solution, but the design is a bit complex and makes it hard to have other electronics pointing dowards (although we’ve seen some solutions to this). So after spending a few days thinking about this, I set out to spend a few fun Fridays looking at solutions for this. The results (so far) are detailed in this post.

Contact charger and decks

The idea I decided to try out was something we’ve been discussion on-and-off for a few years, using contact charging instead of Qi. So I revived a few of our old ideas and starting looking for parts. This is always a fun phase of an idea, the sky is the limit! I’m kind of used to electronic parts, but I find looking for mechanical parts is a lot harder. I don’t really have the vocabulary for this and all the parts look the same size in the product photos…

Finally the solution I wanted to test was using small pogo-pins. The idea is that the pogo-pins could be included into any design of new boards and easily enable charging. With the parts found and the ideas fresh again, I jumped into KiCad (5.99) and started the design. Two different designs were made, pictured below.

4 segments (2xGND and 2x5V)
Two circles, one GND and 5V

Why two solutions? I wasn’t sure which one would work well. I suspected the 4 segments would be better, but I was hoping for the two circles, since you would not need additional electronics to handle 5V/GND switched depending on how you landed.

Since there was two solutions for the pad, there’s also two solutions for the deck. I wasn’t sure about what pogo pins to use, so there’s two different sets of pins (which makes it a bit confusing).

Deck for segmented charger (with diode bridge on back)
Deck for circular charger (without diode bridge)

With the components and PCBs in hand I started testing, and…the results were not promising. The design was made to work with our current Qi charging pad (link). Although the alignment for Qi is important, the tolerance was not as tight as what I needed. Back to the drawing board, this time the mechanical one. I used FreeCAD to create a new version where the Crazyflie would align better. After a few iterations with the 3D printer I ended up with this:

Updated charge pad design
Crazyflie on 3D printed charging pad

Putting it all together, the results were promising. With the new mechanical design the area the Crazyflie can land in is larger and the alignment much better. I think it’s good enough to show that the concept can work. The next step is to look a bit more at the pins, they might be a bit too easy to damage.

Crazyflie on circular charge pad
Charge pads and decks

The Glow deck

I mentioned above that the idea was to be able to put charging on decks with other designs on them. To test out this concept I revived another old idea, the Glow deck! The Glow deck is a design where the LEDs are visible from the side and also a bit stronger. To achieve this a diffuse lens would be mounted on the PCB.

The first version of the Glow I made was years ago. It contained a high-power LED, which was really bright…and really warm. We stopped measuring the PCB temperature at 140 C, so that wasn’t something that was going to work out. It also only had one color and I wanted more.

This time around I decided to try two different versions. The reason for the two versions was that I wanted to see if more power actually added something (except complexity and price) when it came to visual effects while flying. One version uses 4xWS2812B LEDs (the same as on the LED-deck) while the other one uses a RGBW high-power LED. I quickly ran into the same problem as with the charging deck, mechanics. I wasn’t able to find a good size of a diffuse lens. Not wanting to be blocked by this I decided on designing one myself and printing it. Not sure how the size would affect the effect I designed two versions.

2 versions of diffuse lens (3D printed)
Left: WS2812B version, right: high-power RGBW version

I haven’t had the time to try out the high-power LED version yet, but the results for the WS2812B version with the diffuse lens looks promising. It’s hard to see the effect in the images, but at least it shows that it’s possible to see the LED from the sides.

Conclusions

I think both the contact charging and the Glow looks promising and I’m happy with the results so far. But making one prototype is easy, making something that can be manufactured and properly sourced is different. If any of these ever turn into a product, the challenge will be finding the right pins, lens, charge base etc.

For the next step, I’m excited to see the high-power version of the Glow in action. This time around I’ve added proper dimming and temperature measurement :-) If you have any ideas, comments or questions drop a line below!

This week we have a guest blog post from Enrica Soria from the Laboratory of Intelligent Systems Faculty of Ecole Polytechnique Fédérale de Lausanne (EPFL) . Enjoy!

From Star Wars to Black Mirror, sci-fi movies predict a future where thousands of drones will fill our sky. Curving sharply around trees or soaring over buildings, they fly just like a flock of starlings. To turn this vision into a reality, real drone swarms need to increase their autonomy and operate in a decentralized fashion. In a decentralized swarm, each robot makes its own decision based only on local information. Decentralization not only allows the swarm to be more robust to the failure of single individuals, but also removes the dependency from a single computing unit, thus making the swarm more scalable in terms of size.

We at LIS (EPFL) have shown that predictive controllers can improve the safety of aerial swarms by predicting and optimizing the agents’ future behavior in an iterative process. However, the centralized nature of this method allowed us to only control five drones and prevented us from scaling up to a large number of drones. For this reason, we have worked on a novel decentralized and scalable swarm controller that allows the safe and cohesive flight of aerial swarms in cluttered environments. In our latest article, published in IEEE Robotics and Automation Letters (RA-L), we describe how we designed the controller, show its scalability in size, and demonstrate its robustness to noise. We studied the swarms’ performance and compared how it changes in two different environments: a forest and funnel-like environment.

The Crazyflie 2.1 was the perfect platform for our experiments. They are lightweight, modular, and tough. This quadcopter can survive big hits when things don’t go as planned… and, if you work on swarms, things can go wrong!

The fleet of Crazyflies equipped with a single marker.

With our algorithm, sixteen robots were able to fly through an artificial forest that we set up in our indoor motion capture arena. In our previous work, we installed four markers on each quadcopter and used the rigid body tracking from Motive (the Optitrack software). The large volume of our experimental room required the usage of big markers for long-distance detection, which added considerable weight to the drone. Hence, in our new work, we use a single marker per drone. Tracking is supported by the ‘crazyswarm’ package and communication with the entire swarm only requires two radio links. However, despite our model being decentralized, in our implementation robots relay the information to an external brain, which does the computations for them. In the future, all the necessary code will be embedded onboard, removing the dependency on external infrastructure.

Our predictive swarm of Crazyflies flying among obstacles in our indoor experimental room.
Video about the article

This work is a step forward towards the fully autonomous deployment of drone swarms in our cities. By enabling safe navigation in cluttered environments, drone fleets will be able to integrate with conventional air traffic, search for missing people, inspect dangerous areas, transport injured people to hospitals quicker, and deliver important packages right to our doors.

For further details, check out our article here!

We have recently worked on functionality in our web site to generate documentation from source in a few ways that we hope will improve the quality as well as simplify maintenance. We have already written a bit about the log and param documentation in the crazyflie-firmware repository, but we now also added and API reference in the python library as well as generating a list of publications related to the Crazyflie.

The Log and Param documentation

Earlier this year we worked on generating documentation for Log and Params from doxygen comments. We will not dig deeper into this here, but you can read more about it in this blog post. The latest version is available on our web in the repository documentation for the crazyflie-firmware: logs and params

An API reference for our Python library

The Crazyflie Python library is what you might use to create programs that interact with the Crazyflie, for instance to manage small swarms. For a while we have had introductory documentation, and step-by-step guides to show you how to perform, what we think are, common tasks.

However along the way we have also added comments and examples to our code. And this, combined with the way we have structured the library actually enables us to automagically generate reference API documentation. That is, something that shows you everything that is possible to do with the library, all modules and classes, all methods and constants that the library offers.

After some recent work this is now happening and the documentation will now get generated each time we deploy our website!

The API reference documentation can be found in the repository documentation for the library. Please check it out! And be picky, complain where the documentation is lacking! Or if the formatting seems weird! We are trying to get the hang of this and we need you to push us!

Managing publications

Some years ago we started to add publication that are related to the Crazyflie to our Research page, we hope that it might be inspiring to read about all the awesome things that the Crazyflie is used for.

Until now it has been a simple list in markdown but with an increasing number of publications it has become harder and harder to maintain it, finally we have put the work into generating the list from a BibTeX (.bib) file instead. One advantage of the new solution is that BibTeX is a well known format to the research community with lots of tools around to manage BibTeX files while another improvement is that the list will be formatted in a consistent manner (which was not always the case earlier).

If you want to add a publication to the page, simply update the .bib file with your data and create a pull request with the changes. We will merge when appropriate and the publication will become visible on the web after the next deploy, usually within a few days.

Behind the scenes

The code for generating documentation from source tends to spread out over multiple repositories and creates some complexity with a multitude of tools for different languages. It should not be necessary to understand the details and we hope the system will be easy to use for contributors to the code base.

Any questions or comments are welcome.

Happy coding!