Category: Crazyflie

The Flow deck has been around for some time already, officially released in 2017 (see this blog post), and the Flow deck v2 was released in 2018 with an improved range sensor. Compared to MoCap positioning and the Loco Positioning System (based on Ultrawideband) that were already possible before, optical flow-based positioning for the Crazyflie opened up many more possibilities. Flight was no longer confined to lab environments with set-up external systems; people could bring the Crazyflie home and do their hacking there. Moreover, doing research for exploration techniques that cannot rely on external positioning systems was possible with it as well. For example, back in my day as a PhD student, I relied heavily on the Flow deck for multi-Crazyflie autonomous exploration. This would have been very difficult without it.

However, despite the numerous benefits that the Flow deck provides, there are also several limitations. These limitations may not be immediately familiar to many before purchasing a Crazyflie with a Flow deck. A while ago, we wrote a blog post about positioning systems in general and even delved into the Loco Positioning System in detail. In this blog post, we will explore the theory of how the Flow deck enables the Crazyflie to fly, share general tips and tricks for ensuring stable flight, and highlight what to avoid. Moreover, we aim to make the Flow deck the focus of next week’s Developer meeting, with the goal of improving or clarifying its performance further.

Theory of the Flow deck

I won’t delve into too much detail but will provide a generic indication of how the Flow deck works. As previously explained in the positioning system blog post, the Flow deck is a relative positioning system with onboard estimation. “Relative” means that wherever you start is the (0, 0, 0) position. The extended Kalman filter processes flow and height information to determine velocity, which is then integrated to estimate the position—essentially dead reckoning. The onboard Kalman filter manages this process, enabling the Crazyflie to use the information for stable hovering.

Image from Positioning System Overview blogpost

The optical flow sensor (PMW3901) calculates pixel flow per frame (this old blog post explains it well), and the IR range sensor (VL53L1x) measures height up to 4 meters (under ideal conditions). The Kalman filter incorporates a measurement model that describes the relationship between these two values and the velocity of the Crazyflie. More detailed information can be found in the state estimation documentation. This capability allows the Crazyflie to hover, as explained in the getting started tutorial.

Image from state estimation repo documentation

Tips & Tricks and Limitations

If you want to fly with the Crazyflie and the Flow deck, there are a couple of things to take in mind:

  • Take off from a floor with texture. Natural texture like wood flooring is probably the best.
  • The floor shouldn’t be too shiny, and be aware of infrared scattering for the height sensor
  • The room should be well-lit, as the sensor needs to see the texture.

There are certain situations that the Flow deck has some issues with:

  • Low or no texture. Flying above something that is only one plain color
  • Black areas. Similar reason to flying above no texture, but it’s more difficult than usual. Especially with startup, the position estimate diverges
  • Low light conditions
  • Flying over its own shadow

We made a video that shows these types of behaviors, starting of course with the most ideal flying conditions:

Moreover, it is also important to note that you shouldn’t fly too high or yaw too often. The latter will make the Crazyflie drift, as the optical flow cannot be distinguished as being caused by the yaw movement.

Developer meeting about Flow deck

We believe that many of the issues people experience are primarily due to the invisibility of the positioning quality. In many of our examples, the Crazyflie will not take off if the position is stable. However, we don’t have a corresponding functionality in our CFclient, as it is more up to the user to recognize when the positioning is diverging. There is a lot of room for improvement in this regard.

This is the reason why the next developer meeting will specifically focus on the Flow deck, which will be on Wednesday the 6th of December, 3 pm central European time. During the meeting, we will explain more about the Flow deck, discuss the issues we are facing, and explore ways to enhance the visibility of positioning quality. Check out this discussion thread for information on how to join.

Today, we welcome Dimitrios Chaikalis from New York University to talk about their project of cooperative flight. Enjoy!

For our work in cooperative flight, we developed controllers for many tightly coupled drones to fly as a unit. The idea is that, either in a centralized or decentralized manner, it should be possible to treat drones as thrust force and yaw moment modules, in order to allow many small drones to carry objects too heavy for a single one to lift.

It quickly turned out that the Crazyflies, with their small size, open-source firmware, ROS compatibility, and, as we happily found out after hours upon hours of crashes, amazing durability, would be the perfect platform to test our controllers.

We designed and 3D-printed very lightweight, hollow connecting rods that could latch onto Crazyflies on one side, along with a number of lightweight polygons such as squares and hexagons with housings for the other side of the rods on all their faces. This allowed us to seamlessly change between geometric configurations and test our controllers.

We first tested with some symmetric triangle and quad formations.

The above is probably literally the first time our cooperative configuration achieved full position control
The tests on quad-copter configurations started as we transitioned to fully modular designs

Eventually, to make the controller generic, we developed a simple script that could deduce with some accuracy the placement of drones given a small lexicographic description submitted by the tester as a string, essentially denoting a sequence of rods and polygons utilized in the current configuration. Of course, some parameters such as rod lengths, or additional weights that we added to the system (such as a piece of foam attached to the structure), could not be known in advance, but the adaptive controller design ensured that the overall system could still achieve stable flight.

Strangely, the L shape has become a sort of ‘staple’ configuration in cooperative load transportation

We also proved that with more than 3 drones in a configuration, we could optimize the thrusts of the agents such that additional performance criteria could be met. For example, in an asymmetric configuration of 5 drones, one of them had a significantly more depleted battery. Crazyflies provide real-time battery voltage feedback, so we were able to use that in an optimization node running in Matlab on a ground computer, choosing thrust levels such that the depleted agent could be utilized less. This was a significant help, because in many of those experiments, the Crazyflies had to operate at more than 80% of their thrust capacity, so battery life optimization was of the essence.

We used ROS for all the code written for the above implementations, using the Crazyflie-ROS package in order to get battery and IMU readings from all drones and provide thrust and roll, pitch, and yaw rate commands at up to 100Hz.

The corresponding publication can be found here:

In case you want to build on our work, you can cite the above paper as such:

D. Chaikalis, N. Evangeliou, A. Tzes, F. Khorrami, ‘Modular Multi-Copter Structure Control for Cooperative Aerial Cargo Transportation‘, Journal of Intelligent & Robotic Systems, 108(2), 31.

YouTube Link:

When we originally wrote the TDoA3 implementation for the Loco Positioning System back in 2017 we had the idea of adding functionality to also enable the Crazyflies to send UWB packets in some situations, AKA TDoA3 Hybrid mode. We did not have the time to implement that idea back then, but through the years there have been some interest in the functionality and recently I finally got around to do it as a Fun Friday project. Annoying enough it was not that complicated and only took a couple of hours, I should have done it earlier!

We wrote a bit about the hybrid mode in an earlier blog post and there is also a github issue with some discussions on the topic. The short version of the functionality is that a Crazyflie at selected times switches from only passively receiving UWB packets from the anchors, to also actively transmitting packets and doing Two Way Ranging (TWR) with the peers in the network.

One use case is for a Crazyflie to simply participate in the TWR traffic to give it ranging information for improved position estimation. This can for instance be useful when flying outside the convex hull where TDoA positioning degrades rapidly while TWR works pretty well.

Another funky use case is to extend a Loco positioning system by using TWR to fly outside the convex hull and land somewhere. At this point the Crazyflie switches role and acts as an anchor instead by including its position in the transmitted packets and enabling other Crazyflies to use the transmissions for TDoA or TWR position estimation.

It is also possible to go even more dynamic and transmit the estimated position while flying and thus act as a flying anchor. There are complications when doing this with multiple Crazyflies as they use information from each other and the estimated positions probably will diverge if errors are not handled in a proper way, but at least there is now a framework where this type of functionality could be added. See the references to research in the area in the previous blog post.

The implementation is very experimental and has not been merged to master yet, but if you are interested you can find it in the krichardsson/hybrid-mode branch (PR #1330). There are a few new parameters that changes the behavior such as turning on/off transmissions, using TDoA or TWR data for position estimation and what to include in transmitted packets. Please see the implementation and documentation for details. Also note that the hybrid mode functionality is not compiled by default and must be enabled in the build configuration to be available.

It’s been cooking on the slow burner for a long time now, the Crazyflie 2.1-Brushless, or CF21-BL in short. Ever since we got inspired by the tinypepper 1-cell brushless motor controller which showed us a small brushless ESC could be made, we got the idea of integrating brushless ESCs into the Crazyflie. Integrating the ESCs turned out to be easier then we though, but we hade more ideas, we wanted it to be efficient. Due to the FPV market and the toothpick sized category plenty of appropriate size components exist, however none is really optimized for efficiency. So we had to go back to the drawing board, contact suppliers and work with them to try and improve efficiency. This turned out to be a very time consuming task and we are now at a stage where we think we have gotten as far as we can with the resources we have.

Why go brushless?

A brushless setup is better in most aspects but it has some downsides, such as cost and complexity. The brushless motors requires a more complex design and is therefore more expensive to manufacture. It also requires a more sophisticated motor driver that also needs a larger PCB board space. On the upside we have better power to weight ratio, better longevity and efficiency to name a few. For the tiny type of brushless motors that are interesting here the efficiency gain is not so obvious though. This is mainly because it is hard to make an efficient motor driver due to the low inductance in the motors and this can definitely be further improved, perhaps with software upgrade of the ESC firmware in the future.

Let’s dive into the current specification!

After many prototypes this is where we are at now:

  • Crazyflie 2.1 base design using the PCB as the frame.
  • 4 x integrated 1-cell 5A ESCs running BLHeli_S/Bluejay
  • Weight: 32 grams ( including 350mA battery)
  • 4 x 08028-10000KV high-quality motors generating up to 30 grams thrust each
  • Custom-designed and optimized 55mm propellers with 35mm pitch
  • Over 10 min hover time in 32-gram configuration (~5g/W efficency)

The added thrust and the longevity of the brushless motors are probably the key features of the CF21-BL. This will improve payload capability or agility for applications where this is needed as well as the robustness. It will come at the expense of a higher price tag though.

The Crazyflie 2.1-Brushless has come a long way but there are still many things that have to be done before it will be available in the store and it is too early to talk about any timeline, but the goal is to release it during 2024!

It seems that many of you are very interested in simulation. We might have gotten the hint when we noticed that our July’s development meeting had our best attendance so far! Therefore, we will be planning a new developer meeting to discuss the upcoming plans for supporting simulation for the Crazyflie.

Getting Started with Simulation tutorial

Perhaps you are not aware, but there is actually a Getting Started tutorial for simulation that has been available for a little over 2 months now. Unfortunately, circumstances prevented us from writing a blog post about it, but we’ve noticed that not all of you are aware of it yet!

The getting-started tutorial demonstrates how to set up the Webots simulator, which already includes Crazyflie models and some cool examples:

  • An example that you can control the Crazyflie with the keyboard
  • An example that the Crazyflie does wall following autonomously

The latter is based on the example app layer for wall-following in the crazyflie-firmware repository. Starting this year, there’s also a Python library equivalent available.

The tutorial concludes with instructions on how to edit these controllers. Alternatively, you can choose to run the files directly from the crazyflie-simulation repository. After completing the tutorial, you can explore the simulation repository documentation for more information and to access additional examples.

Upcoming plans

With so many plans and so little time! This is a common phrase at Bitcraze, and it’s a symptom of being an overly ambitious, but too small, team. By the way, we are still looking for more people :). Nonetheless, we have big plans to take our Crazyflie simulation to the next level:

  • ROS 2 Crazyflie model for Webots: The Crazyflie has been a part of the Webots standard robots for 2 years now, but we still need to implement the Crazyflie into the Webots ROS 2 repository.
  • Better (new) Gazebo support: Currently, we only have a very simple example for Gazebo, which is limited to motors with no control input. Working with the C++ API can be a bit challenging, so it might be worth considering the use of ROS 2 in the loop here. Let’s see what comes out of it.
  • Integration into Crazyswarm2: Once the Webots ROS2 node has been released, integrating the Crazyflie simulation into Crazyswarm2 will become more straightforward.
  • Improvement to the Python bindings: We’ve had Python bindings for controllers and the high-level commander for a while. Recently, we also added Python bindings for the estimator (currently for loco positioning only). However, there are still some issues to address with the Python bindings for the controllers due to timing issues with the simulators.
  • Linking with our CFLIB: Currently, both Webots and the Crazyflie Python library use entirely different APIs. This means that these scripts are not compatible and you’ll need to be creative not to reuse new code. However, wouldn’t it be nice to use a python example from the python library with a --sim and that it would actually control the Crazyflie in the simulator instead?

Of course, there are probably more improvements that we haven’t thought of yet, but that’s why we have developer meetings!

Come and join us at the Developer meeting.

We will be hosting another developer meeting on November 1st at 15:00 Central European Time (accounting for the time-shift from summer to autumn). You can find details on how to join in the discussion thread here.

Just for your information, I (Kimberly) am the main driving force behind our simulation efforts. However, I’m currently on partial sick leave and will soon be on full leave for a while. I kindly ask for your patience with the pace of ongoing developments. Remember, it’s an open-source project, so if you’d like to contribute and help out, we would greatly appreciate it :)

We talked about it before the summer, and it’s finally here! The 350 mAh battery is now available in our shop. It implies some changes in the products we offer, so here is a breakdown of what’s new:

The 350 mAh battery

It’s here!

It is more powerful than the 250 mAh battery that comes with your Crazyflie. We based it on the Tattu 350mAh 3.7V 30C 1S1P but with some custom works like gold connectors, tailored wire length, and awesome Bitcraze graphics on it. On top of the added power, the upgrade has higher capabilities, (30C burst current, which is more than 10 Amp) and higher energy density (~130 Wh/kg instead of ~105 Wh/kg). It all means that this could boost your hover time up to 10 minutes, and you’ll have more punch during acceleration! It is, though, more expansive than the 250 mAh.

The pin headers

The 350 mAh is thicker than the stock battery, which means you would need longer pin headers in order to snug it onto your Crazyflie. For that, there are now 9mm pin headers available in the shop. This means that now, you can get 3 different male connectors:

  • the 8+14mm is the one that comes with your Crazyflie kit. It’s meant to be phased out at some point. It allows to fit 1 or 2 decks and the 250 mAh battery.
  • the 9+15mm is slightly longer and is available in the shop – both as a spare part and in the upgraded battery bundle. It allows to fit 1 or 2 decks and the 350 mAh battery.
  • the male long connector: the longest pin of all, it’s the one that allows you to fit 3 decks.

Since it makes more sense to have slightly longer pins, the male connectors as spare parts are now slightly longer ones than those you get in your Crazyflie kit.

If you’re not sure, you can always buy the upgraded battery bundle that offers the 350 mAh battery with the right pin headers.


The 350 mAh battery is much more suited for swarms than the 250 mAh, that’s why we’re planning on having an upgraded offer for our swarm bundles. In the coming week, both the Lighthouse and the Loco Swarms will be fitted for the updated offer. That would mean that it will include the new batteries with the right pin headers as well – there will be a slight price increase to match the price of the batteries.

Bare PCB

But that’s not the only surprise waiting for you in the shop: you can now also buy a spare Crazyflie PCB! We thought it would be good to have this option in the store – in case you have crashed too many times and you only just need the PCB!

Right now, it may seem a little confusing, between our different propellers, batteries, or pin headers. It’s mainly because we are trying to, slowly, build up a better, upgraded offer – which will, eventually, culminate in an upgraded Crazyflie 2.1, where the 47-17 propellers and the 9mm pin headers are standards. We’re also planning to publish a guide to help you quickly figure out what would best suit your needs!

Today, Suryansh Sharma from TU Delft presents the open-source Gimbal they devised. Enjoy!

Crazyflies (and other drones in this weight class) are extremely fun to fly and prototype with! But if you are also a scientist or tinkerer and not a well-skilled drone pilot then you might struggle with flying these platforms especially when testing new control loops or experimental code. While crashing also teaches a lot about the behavior of the system, sometimes we are interested in seeing the system dynamics without breaking the drone.

Currently, doing this for such small drones is not easy. We need something lightweight and still accessible. To solve this, we made Open Gimbal: a specially designed 3 degrees of freedom (DoF) platform that caters to the unique requirements of these tiny drones. We make two versions, (a) Tripod version which can be mounted on a camera / light tripod with a screw thread of sizes 1/4-20 UNC or 3/8-16 UNC (b) Desktop version which can be placed on a table top.

Our approach focuses on simplicity and accessibility. We developed an open-source, 3-D printable electro-mechanical design that has minimal size and low complexity. This design facilitates easy replication and customization, making it widely accessible to researchers and developers. The platform allows for unrestricted and free rotational motion, enabling comprehensive experimentation and evaluation. You can see the movement from the CAD version below:

Degrees of Rotational freedom that Open Gimbal provides

You can also check out the interactive CAD model and see how the gimbal moves here. All of the 3D model files as well as the BOM and instructions for assembly can be found in our repository here.

In our publication, we also address the challenges of sensing flight dynamics at a small scale. To do so, we have devised an integrated wireless batteryless sensor subsystem. Our innovative solution eliminates the need for complex wiring and instead uses wireless power transfer for sensor data reception. You can read all about how we do this in our paper here.

If you do end up using the platform for research then you can cite us using the details below:

@ARTICLE{10225720, author={Sharma, Suryansh and Dijkstra, Tristan and Prasad, Ranga Venkatesha}, journal={IEEE Sensors Letters}, title={Open Gimbal: A 3 Degrees of Freedom Open Source Sensing and Testing Platform for Nano- and Micro-UAVs}, year={2023}, volume={7}, number={9}, pages={1-4}, doi={10.1109/LSENS.2023.3307121}}

I hope that you find the Open Gimbal useful! Feel free to reach out to me at if you have any ideas/questions or if you end up making an Open Gimbal yourself!

For this months developer meeting we will be discussing the increasing complexity of the deck subsystem, talking about the challenges we see moving forward as well as discussing some possible solutions. In the blog post this week I’ll be discussing the first part, some of the challenges moving forward.

After releasing the Crazyflie Nano Quadcopter back in 2013 we realized we wanted something more than the small expansion connector we placed there. Sure, it was possible to attach more electronics (and we did) but mechanically, electrically, and software-wise it was a hassle. So when we got around to working on the Crazyflie 2.0 in 2014 we were really happy with the new expansion connector. The goal was to offer users new hardware solutions as they became available as well as giving the possibility to create customized hardware configurations. It supported multiple buses, different heights for the decks, top and bottom attachment, automatic detection, and last, but not least, it held the battery in place. Initially, we released a few decks, but over the years there have been many more. Some are very simple, like the breakout, and some are very complex, like the AI deck.

Although we’re still very happy with the deck subsystem, we’re starting to see some challenges moving forward as deck complexity increases. Some of these issues are:

  • Resource sharing in the STM32: Mainly DMA conflicts between different peripherals, like conflict for DMA using DSHOT on the Crazyflie Bolt and the WS2812b driver for the LED-ring.
  • Bus arbitration and performance: Some decks make excessive use of some buses, which can cause issues with certain combinations like the LPS deck and micro-SD card deck.
  • Deck combinations and pins: As more interesting decks are released and we’re able to carry more weight, users want to combine more decks. Although we try to be smart with pin allocation there’s a limit on how you can combine the decks.
  • MCUs on decks: With increased complexity also comes separate MCUs on decks. Although working well for offloading the main MCUs on the Crazyflie, the complexity quickly increases both for usage and for development. This is something we’ve seen with the AI deck for instance, which contains 2 MCUs.

The challenges above are something we discuss from time to time around the office, often ending up at a whiteboard pitching various ideas. The most popular solution (and therefore the most likely one becomes reality) is moving complexity off the Crazyflie and onto MCUs placed on the various decks. This would probably solve most of the challenges with the first three points, but obviously worsen the last point above.

So the question quickly becomes, how do we work with multiple decks with one (or more) MCUs without the situation becoming too complex? Something often heard in embedded is that for each MCU added the complexity grows exponentially. From experience, we can say that this isn’t so far off.

With the new Crazyradio 2.0 out, discussions on new protocols, the possibility of a library rewrite, new deck prototypes and more payload capabilities, it’s becoming more clear how this would fit in. We still haven’t decided on what solutions we’re using, but we do have a bunch of ideas that we think would fit together to meet these challenges moving forward. On Wednesday I’ll continue to discuss some of these challenges in our monthly developer meeting and also discuss some of the suggested solutions. We’re also more than happy to hear comments about this from our users. If you’re interested in joining the discussion you will find the link here. We hope to see you there!

Lately we have been thinking about and using the Rust programming language more at Bitcraze. In this blog post we will talk a bit about our current use, current experiments and potentially future use and how it will affect our ecosystem.

Rust is a system programming language that has good performance, is reliable and productive. Practically it means that it can be used to run small and fast code (well suited for embedded systems for example), be quite fun to write, and be reasonably sure that if it compiles, it works.

On servers

Over the year we have written and maintained a server system to handle a lot of things related to production and sales. This system is the one generating shipping quote when you order in our store, telling us that there is an order, printing packing lists and shipping labels for the order as well as keeping track of stock and telling us when it is time to order a new batch of product.

This system is used every day and has been invaluable to how we work at Bitcraze. It is mostly implemented as NodeJS micro services.

We have started writing new functionality for it in Rust instead of in a new monolithic service. This has been a great experience, not always easy, but the bonus is that once it compiles there has been almost no run-time error. This has allowed us to gain experience with Rust in an environment that is well documented: servers on PC.

In test rigs

Every manufactured product must be tested: there is no guarantee a board will work when it exits the re-flow oven. This test usually happens in a test-rig that measures and affects various signals on the board (look under your favorite Bitcraze deck and you will see test-points: round pads designed to enter in contact with test probes). Attached to this test-rig is a computer running our test software. We have used a Python-implemented test software for all our products so far and this system started showing its age by being harder and harder to work with and, most importantly, hard to deploy on computers in the factory.

For Crazyradio 2.0, we decided to completely re-write our test software, in Rust of course :-). The design of the test framework is very inspired by OpenHTF: the framework provides the basic architecture of the test and the executor, tests are implemented in Rust and implement all the required test phases. Test statuses are streamed to a web browser as well as to our server (to one of the newer parts of our server system written in Rust). There are two big advantages of using Rust in this application: making sure the test software works reliably and without errors saves a lot of time during manufacturing and helps make sure no bad board leaves the factory. Rust is also awesome to deploy and distribute: the software written on our Linux machine can be compiled for Windows/Mac/Linux on any architecture, no more Python environment to set up!

As for the deployment we actually choose to deploy the test software on a Raspberry pie managed by Balena cloud. This means that we can remotely update the test rig software and we are always sure that the right version is running in production. Rust has allowed that to be painless: we develop on our amd64 PCs and it compiles out-of-the-box and works on the ARM64 Raspberry Pi.

In embedded systems

Now we are coming to our more experimental use of Rust, until now on fun-Fridays project but soon on prototypes. We have been playing with Rust on embedded for quite a while: I have re-written the Crazyflie2’s stm32 boot-loader in Rust, we have experimented with Rust on a couple of our ESP32-based prototypes. Embedded systems are never as easy as programming on PC and the way Rust libs are organized to guarantee good usage of the peripheral does not always yield good error messages from the compiler. But, for sure, it does not feel good and it feels very scary to come back to C: the Rust compiler checks so many things that it makes programming fun, with C, any small mistake will bite hard a couple of weeks later.

We have just started working seriously on a new deck (more about it in a future blog post ;-) and we have started in Rust. We do still take that as an experiment: we keep our options open to coming back to C if there is any hiccup. But so far it looks quite good.

In the Crazyflie lib?

That is a future plan, that we have not started to work on seriously at all, but that we are planning for the future. We are planning to write a new version of the Crazyflie lib in Rust with binding to other languages.

According to our experience so far, Rust is safe, fun to write, and very easy to distribute to all the systems we currently support with the Python lib and more. On top of Windows/Mac/Linux, Rust would enable support of our official lib on the web, in embedded systems (ie. ESP32), as well as on iPhone and Android.

The plan would be to have the low level of the lib, ie. communication with Crazyradio and the Crazyflie and subsystems drivers, implemented in Rust. Then binding to Python, C++, Ros, Javascript, … can be made to allow usage of the lib in these languages. This would have the advantage of allowing every current user to use the official lib without having to re-implement their own special-purpose version. On the Python side, nothing would change, in the sense that a Rust-implemented lib can be installed with “pip install cflib” …


This blog post is a request for comments: if you are a user of the Crazyflie and have strong opinions for or against Rust we would like to hear about it. We want to make it clear that we are not planning on porting the Crazyflie firmware to Rust: the Crazyflie is designed as a development platform and we are aware that Rust is not yet as used or well-known as C or Python. However, the firmware running on a deck CPU or in the bottom of the lib would benefit a lot from Rust’s advantages and do not need to be modified so often outside Bitcraze (it is of course always open-source and we encourage contributions :-D).

We will keep you updated if we make more progress on the new deck and the lib, in the meantime we will keep having fun experimenting :-).

The python client is based on Qt and we have been using version 5 so far. Qt5 has been replaced by Qt6 quite some time ago and as Qt5 is not really maintained any more, we have been looking at switching to Qt6 for a long time. Finally we have taken the step, this blog post will outline what has changed.

The switch to PyQt6 is not that complicated for the majority of the client code base, apart from some minor changes in various classes, the biggest update is that enumerations are handled differently. If you check out the changes in the pull request you will see that imports have changed for obvious reasons

# PyQt5
from PyQt5.QtCore import Qt
from PyQt5.QtWidgets import QMessageBox
# ...
# PyQt6
from PyQt6.QtCore import Qt
from PyQt6.QtWidgets import QMessageBox
# ...Code language: Python (python)

Enumerations are not available directly on the Qt type anymore, instead they must be named using their fully qualified names. For instance

# PyQt5
# PyQt6
Qt.ItemDataRole.DisplayRoleCode language: Python (python)

And some other minor changes.

The Qualisys tab

The main blocker for doing this change has been that the Qualisys tab was using a library that used to be available in PyQt5 but that has not been added to PyQt6, we ended up removing the Qualisys tab to be able to move on. The Qualisys tab was originally contributed by Qualisys for demo purposes and it had two nice areas of functionality that we would like to re-implement:

  1. Motion capture positioning. It was very easy to connect a Qualisys mocap system and feed the position information to the Crazyflie for automated flight.
  2. Trajectory support. It was possible to fly some simple trajectories, for instance a circle which is nice for testing and demos.

The idea is to re-implement the mocap connectivity using the libmotioncapture library from Crazyswarm2, as an extra bonus this would support all major mocap systems. For the trajectory part, we would like to add this as a new tab that can be used by any positioning systems, mocap, Loco, Lighthouse or flow deck. If you are interested in helping out with this, let us know!

The state of the code

The changes for PyQt6 have been merged into the master branch. It seems to work but we have not tested all functionality yet, please let us know if you run into any problems or weird behavior.

When you have pulled in the code from github you will also have to re-install all dependencies by running

pip install -e .