Santa is soon to be knocking on the door, hopefully with one or two exciting toys (with blinking LEDs) for us geeky people! There will not be a Christmas video in the Bitcraze gift this year, instead we’re wrapping up a new release that we hope will add to the Christmas fun!
We have been working on a secret project though and there might be a video for next week’s blog post showing what we have been up to…
The 2022.12 release
We are happy to announce that a new official release is out, 2022.12! We have mainly fixed bugs and stability issues but also added some new features, please see details below.
One of the main events in this release is that the Flapper Nimble+ has got official support with the flapper platform, it can now be flashed through the client like any other member of the Crazyflie family. A new controller, based on work by Brescianini has been added. The Kalman estimator and Lighthouse system have been tweaked to work better with the increased data volumes generated with 2+ base stations. Some improvements for brushless motors have been added. Finally there have been some general bug and stability fixes, including improvements for flashing of the AI-deck.
Please see the release notes for a list of all changes.
A blocking method has been added to upload trajectories to the high level commander, the various Uploader classes in the examples are not needed anymore. Stability and bug fixes related to deck flashing.
Please see the release notes for a list of all changes.
A button has been added in the console log tab to get statistics about persistent storage in the Crazyflie. The final traces of Windows and Mac builds have been cleaned out and some stability and bug fixes have been applied.
Please see the release notes for a list of all changes.
As some of you may know, I’ve worked at Bitcraze for two summers (2019, 2020), and I did my Bachelor’s thesis here during the spring this year. While we mentioned shortly that I started working on my thesis (here), I never presented the results of it, so I thought that I’d do that now! Better late than never, right?
So, during my thesis I built a prototype deck for the Crazyflie which contained five multizone lidar sensors (VL53L5CX) and an ESP32-S3. The VL53L5CX sensors can output distances to a 8×8 grid, with a 45 degrees FoV at a rate of 15 hz. The purpose of the ESP32-S3 was to collect the data from the sensors and send it to a ground control station, either with WiFi, or, with the nRF radio on the Crazyflie. While the ESP32-S3 is quite overkill for only collecting data and send it, we weren’t sure of how much data that would be gathered from the sensors, so to be on the safe side we rolled with the ESP32-S3. Both the sensors and the microcontroller was very new at the time so it seemed like a good oportunity to try them out.
I designed the schematic in KiCad and got a lot of help from everyone here at Bitcraze while doing so, especially Tobias. Once the schematic was done I designed the PCB, ordered the components and then waited eagerly for the stuff to arrive. Once everything had arrived, I soldered all components and assembled the deck. I then wrote some firmware for the ESP32-S3, and the STM32 on the Crazyflie, and at last I wrote a simple GUI in PyQt to help visualize the data, both in 2D and 3D.
The deck was quite successful and while the GUI was very far from perfect, I think it did show that the deck has some nice potential and it was very cool to see the 3D point cloud in realtime while flying the Crazyflie! I tried sending the data over WiFi which worked perfectly well, and I also tried sending it through the nRF on the Crazyflie with the help of CPX, which also worked pretty well.
If you’re more curious about the thesis, feel free to check it out here, and the github repository can be found here.
I finished the thesis in the beginning of the summer, and I have been working part time here at Bitcraze since September and I’ve truly been loving! I think it’s been really cool to become a part of the team and work more on the regular stuff that the rest of the team does. It has been very interesting to see how the team works and cooperates on a daily basis. Something that striked me was just how many products and different features and services we handle here, with only six people!
Fortunately and unfortunately, I will be moving to Gothenburg next week which means that my time at Bitcraze is over, for this time. I have learned a lot from everyone here and truly appreciate all the love and support, which actually started before I even started my Bachelor’s degree.
The communication protocols between a PC, a Crazyradio and a Crazyflie are critical parts of the Crazyflie ecosystem, they allow to communicate with and control the Crazyflies in real time. These protocols have been documented in a couple of blog posts already. They exist since the origin of the Crazyflie, in 2011, and where originally designed with one use-case in mind: controlling one Crazyflie manually from a game-pad connected to a PC. The Crazyflie can of course do much more nowadays, like flying in big autonomous swarm, but the underlying communication protocols are still an evolution of these simple manual-flights single Crazyflie origin.
Over time we have felt the limitations of the communications protocols and of the Crazyradio (PA). For this reason, lately, we have been starting to work at making a new, more modern, Crazyradio dongle and at revamping the communication protocol used to communicate with the Crazyflie. The aim is to start with the current Crazyflie use-cases including flying in centralized and decentralized swarms with varying levels of autonomy of the drone itself.
The first project is to make a new Crazyradio dongle: the current Crazyradio PA is based on an old nRF24 chip from Nordic semi. It runs on a 8051 microcontroller and has a mostly hardware-driven radio. This means that the processing power is quite limited and the radio has no flexibility with the on-air protocol and packet size limited to 32 Bytes. We are working on a new Crazyradio dongle based on an nRF52840 microcontroller and a RF power amplifier. We expect the new radio to be available sometimes before the summer 2023:
The main advantage of using the new nRF52 microcontroller is that it is an ARM Cortex-M4 chip with quite a lot of flash and ram. This will make development much easier and faster. It is also a much more capable chip which will improve communication performance. The output power will be similar to the Crazyradio PA so the range should be similar. The radio being more flexible, it will allow development of new protocols including the capability to send packets bigger than 32 bytes.
On the USB protocol side, we will take this opportunity to improve the USB protocol. We are making it more flexible so that it can be expanded more easily in the future and it will also be much more efficient when controlling swarm of Crazyflies.
The first version of the new Crazyradio will implement the same air-protocol as the current one, so there will not be a need to change the Crazyflie firmware right away.
However we are already thinking of a couple of new radio protocol that we want to develop for the new Crazyradio and the Crazyflie 2:
A low latency channel hopping protocol: This protocol would allow to connect one or a swarm of Crazyflie using channel hopping. This means that the user does not have to setup a channel for communication anymore, the protocol will automatically hop form channel to channel randomly. This will make it much easier to connect to Crazyflies and make the link more reliable
A P2P protocol that will allow Crazyflies and Crazyradios to talk to each other: the main idea is to make the P2P protocol a proper supported protocol and to make the Crazyradio able to be a node in the P2P network. This should simplify a lot the development of autonomous swarm.
On the higher level protocol, CRTP, we are stating to think of ways to make new protocols as well. On that side, there has been no work started yet but a lot of ideas and general direction based on our experience and on feedback in iROS 2022 and other conferences. The basic lose ideas currently are:
Integrating the concept of connection in the protocol: currently there is no such concept so for example if a logging is setup and the link is lost, the logging subsystem will continue to try to send packets forever. A more logical implementation would tell the logging subsystem that the connection is lost and so that the logging can be canceled.
Basing the protocol on Remote Procedure Call: A lot of that we currently do in CRTP is to emulate procedure call with packets and parameters. Making procedure call the base unit of the protocol would make it much easier to use and extend
Versioning! One of the problem currently is that without clear versioning, it is very hard to make the protocol evolve in a documented way. We will find a way to version so that we can improve, add and remove functionality when needed.
Finally. We are not planning on running (micro) ROS in the Crazyflie 2, however the goal is to make a protocol that would make the interface to (micro) ROS and Crazyswarm as thin and boring as possible. Today the Crazyswarm ROS Crazyflie server is a full fledged client, the hope is to make the Crazyflie protocol in such a way that it would look more like a proxy to the Crazyflie RPC API.
If you have made a client that communicates directly with the Crazyradio PA, the change in the new Crazyradio will affect you. We will soon make the new Crazyradio 2 repos public with documentation of the new protocol to give the possibility to have discussions before release.
Those are still very lose ideas and the main goal of this blog post is to bring awareness to the future work: if you have any ideas, opinion or wishes when it comes to the communication protocol please come in contact with us and let’s discuss. The best forum is our github discussion page. Also we are planning to have an online townhall meeting so that we can handle any questions about implementation or discuss the proposed protocol, so keep an eye on this discussion thread: Townhall meeting (7 Dec 2022) · Discussion #426 · bitcraze (github.com).
IROS in Kyoto is over and all Bitcrazers are finally back in Sweden again. We had a really good time in Japan and enjoyed all the interesting discussions we had with all of you, thanks!
The demo has similarities with our previous demo (see IROS 2019) but has been upgraded to be a fully autonomous and decentralized swarm with 9 Crazyflies buzzing around in a cage, going back to charging pads for wireless charging when the battery is running out. The demo supports multiple Crazyflies flying at the same time, avoiding collisions without a central authority, all decision making is done in each Crazyflie, that is fully decentralized.
The hardware is off-the-shelf products available in our store (links here). The software is obviously written specifically for the demo, but we wanted to use the building blocks already available in the system so the demo code is mainly “glue” to connect them together.
The cage/flying space
The flying space was box shaped, 3×2 meters in foot print and 2.5 meters high. We enclosed it in our lightweight travel cage made from aluminium pipes and a light net. It is a pretty small space to fly multiple Crazyflies in at the same time but it worked! The main problem with such a small space is down-wash from other Crazyflies and having enough room to avoid collisions. 3 Crazyflies worked pretty well, but had the space been larger it would have been possible to fly all nine.
Localization
Localization was handled by the Lighthouse positioning system. We used two base stations and the lighthouse deck on each Crazyflie which provides the Crazyflies with their current position with high accuracy.
Since the position is computed in the Crazyflie, using only data from on-board sensors, no external communication is needed in relation to the localization system. The only exception was that we uploaded the physical geometry of the system when setting up the cage.
Path planing
When a Crazyflie is flying in the demo, the standard mode of operation is to fly a randomized pattern of straight lines. From time to time (randomized) the Crazyflie can also chose to fly the spiral that we have used in earlier demos (see the IROS 2019 demo for instance).
When the battery is running out, the Crazyflie goes back to the charging pad for charging. The position is sampled before taking off and this coordinate is used as the landing point to find the charging pad. When landed the Crazyflie verifies that the battery is being charged. If the battery is not charging the Crazyflie assumes it missed the charging pad and it takes off again to adjust the position.
Charging
The Crazyflies were equiped with the Qi-charging deck for wireless charging. The charging pads are 3D-printed pads with a slope to make the Crazyflie slide into position also if the landing is not perfect. In the center of the pads there are standard Qi-chargers from IKEA mounted to provide power.
To fly continuously, the system charging rate must be higher than what is consumed by the flying Crazyflies. With a system of nine Crazyflies that are charging through Qi-chargers it is possible to keep one Crazyflie flying, just. To get some margin we increased the charging speed a bit, the down side being that the Crazyflies get warm and the batteries ware out faster.
Collision avoidance
We use the built in collision avoidance system contributed by James Alan Preiss at University of Southern California. Thanks James, it works like a charm!
There is no planing ahead, but each Crazyflie must know where the other Crazyflies are located. Based on this information they avoid each other and chose a new path to reach their target position. For this to work each Crazyflie is continuously broadcasting its position to the other Crazyflies using the peer-to-peer framework.
Swarm control and collaboration
As mentioned earlier there is no central authority that decides which Crazyflie that should take off or go to a specific position, instead this functionality is handled in each Crazyflie. To make it possible for each Crazyflie to have a rough idea of the system state, each Crazyflie is broadcasting its position and state (landed, flying etc) to the other Crazyflies. If a Crazyflie realizes that too few drones are flying, it will simply take off to fix the problem, if it sees that too many are flying it will go back to the charging pad. To avoid that all Crazyflies takes off or lands at the same time, a randomized hold-back time is used before the actions is executed. This does not fully prevent two individuals from taking off at the same time, but makes it less likely, and eventually the correct number of drones will fly.
The number of drones that should fly at the same time is a system wide parameter that can be set from one of the peers in the system. To make sure they all agree on the value, a simple mechanism is used based on the age of the data. The value and the age of the value is included in the broadcast data. When another Crazyflie receives the data it compares the age of the received data with the age of the data it already has and replaces it only if it is younger.
Sniffer
A tenth Crazyflie is used in the demo as a sniffer. It is essentially a non-flying member of the swarm that listens to the broadcast traffic and it is used to feed data to a GUI that displays the state of the system. It can also be used to inject a new value for the desired number of flying Crazyflies.
Implementation and how to run it
The code is mainly implemented as an app in the Crazyflie firmware, using the app layer. The main part is a state machine that keeps track of what to do next with some other modules handling communication and trajectories.
The examples/demos/decentralized_swarm/src/common_files/choose_app.h file controls if the code is compiled for a swarm member or the sniffer.
All Crazyflies should have the same radio channel and the same address, except the last byte. Swarm members must use addresses ending in 01 to 09 while the sniffer must use the address ending in 00.
As you probably noticed already, this summer I experimented with ROS2 and connecting the Crazyflie with multi-ranger to several mapping and navigation nodes (see this and this blogpost). First I started with an experimental repo on my personal Github account called crazyflie_ros2_experimental, where I managed to do some mapping and navigation already. In August we started porting most of this functionality to the crazyswarm2 project, so that is what this blogpost is mostly about.
Crazyswarm goes ROS2
Most of you are already familiar with Crazyswarm for ROS1, which is a project that Wolfgang Hönig and James Preiss have maintained since its creation in 2017 at the University of Southern California. Since then, many have used and referred to this work, since the paper has been cited more than 260 times. From all the Crazyflie papers of the latest ICRA and IROS conferences, 50 % of the papers have used Crazyswarm as their communication middleware. If you haven’t heard about Crazyswarm yet, please check-out the nice BAMdays talk Wolfgang gave last year.
Unfortunately, ROS1 will not be there forever and will be phased out anno 2025 and will not be supported for Ubuntu 22.04 and up. Therefore, Wolfgang, now at the Intelligent Multi-robot Coordination Lab at TU Berlin, has already started with the ROS2 port of Crazyswarm, namely Crazyswarm2. Here the same principle of the C++ based Crazyflie server and the python wrapper were been implemented, along with the simple position based simulation and Teleop nodes. Mind that the name Crazyswarm2 is just the project name out of historic reasons, but the package itself can also be used for individual Crazyflies as well. That is why the package names will be called crazyflie_*
Porting the Summer Hack project to Crazyswarm2
The crazyflie_ros2_experimental was fun to hack around, as it was (as the name suggests) experimental and I didn’t need to worry about releases, bugfixes etc. However, the problem of developing only here, is that the further you go the more work it becomes to make it more official. That is when Wolfgang and I sat down and started talking about porting what I’ve done in the summer into Crazyswarm2. This is also a good opportunity to get more involved with the project, especially with so many Crazyfliers using the ROS as well.
The first step was to write a second crazyflie_server node that relied on the python CFlib. This means that many of the variables I used to hardcode in the experimental node, needed to be defined within the parameter structure of ROS2. The crazyflies.yaml is where anything relevant for the server (like the URIs and parameters) needs to be defined. Both the C++ backend server and the CFlib backend server are using the same parameters. Also the functionality of the both servers are pretty similar, except for that logging is only possible on the CFlib version and uploading/follow trajectories is only possible on the C++ version. An overview will be provided soon on the Crazyswarm2 documentation website.
The second step was to make the crazyflie_server (cflib) node suitable to be connected to external packages that I’ve worked with during the hack project. Therefore, there are some special logging modes, that enables the server to not only output topics based on logging, but Pose/Odometry/LaserScan messages along with Transforms. This allowed the SLAM_toolbox to use the data from the Crazyflie itself to create a map, which you can see an example of in this tutorial.
Moreover, for the navigation it was important that incoming Twist messages either from keyboard or from a navigation toolkit were handled properly. Most of these packages assume a 2D non-holonomic robot, but a quadcopter like the Crazyflie needs to first take off, stay in the air and land. Therefore in the examples, a separate node (vel_mux.py) was written to receive incoming Twist messages, first have the Crazyflie take off in high level commander, and keep sending hover commands to keep it in the air until a land service is called.
What’s next?
As you probably noticed, the project is still under development, but at least it is now at a good state that we feel comfortable to presented at the upcoming ROScon :) We also want to include an more official simulation package, especially now that the Crazyflie has recently became part of the official release of Webots 2022b, but we are currently waiting on the webots_ros2 to be released in the ubuntu packages. Moreover, the idea is to provide multiple simulation backends that based on the requirement of the topic (swarms, vision-based etc), the user can select the simulation most useful for their situation. Also, we would like to even out the missing items (trajectory handling, logging) in both the cflib and cpp backend of the crazyflie_server so that they can be used interchangeably. Also, I saw that the experimental simple mapper node has been featured on social media, so perhaps we should be converting that to Crazyswarm2 as well :)
So once we got the most of the above mentioned issues out the way, that will be the time that we can start discussing the official release of a ROS2 Crazyflie package with its source code residing in the Crazyswarm2 repository. In the meantime, it would be awesome that anybody that is interested in ROS2, or want to soon upgrade their Crazyswarm(1) packages to ROS2 to give the package a whirl. The more people that are trying it out and report bugs/proposing fixes, the more stable it becomes and closer it will come to an official release! Please join us and start any discussions on the Crazyswarm2 project github repository.
Before the summer vacations, I had the opportunity to spend some time working on AI deck improvements (blog post). One of the goals I set was to get CRTP over WiFi working, and try to fix issues along the way. The idea was to put together a small example where you could fly the Crazyflie using the keyboard and see the streamed image along the way. This would require both CRTP to the Crazyflie (logging and commands) as well as CPX to the GAP8 for the images. Just before heading off to vacation I managed to get the demo working, this post is about the results and som of the things that changed.
Link drivers
When using the Crazyflie Python library you connect to a Crazyflie using a URI. The first part of the URI (i.e radio or usb) selects what link driver to use for the connection. For example radio://0/80/2M/E7E7E7E7E7 selects the radio link driver, USB dongle 0 and communication at 2Mbit on channel E7E7E7E7E7.
While working on this demo there were two major things changed in the link drivers. The first one was the implementation of the serial link (serial://) which is now using CPX for CRTP to the Crazyflie. The usecase for this link driver is to connect a Raspberry Pi via a serial port to the Crazyflie on a larger platform.
The second change was to add a new link driver for connecting to the Crazyflie via TCP. Using this link driver it’s possible to connect to the Crazyflie via the network. It’s also possible to get the underlying protocol, the CPX object, for using CPX directly. This is used for communicating with for example the GAP8 to get images.
In the new TCP link driver the URI starts with tcp:// and has either an IP or a host name, followed by the port. Here’s two examples:
tcp://aideck-AABBCCDD.local:5000
tcp://196.168.0.100:5000
Comparison with the Crazyradio PA
So can WiFi be used now instead of the Crazyradio PA? Well, it depends. Using WiFi will give you larger throughput but you will trade this for latency. In our tests the latency is both larger and very random. In the demo I fly with the Flow V2 deck, which means latency isn’t that much of an issue. But if you were to fly without positioning and just use a joystick, this would not work out.
The Demo!
Below is a video of some flying at our office, to try it out yourself have a look at the example code here. Although the demo was mostly intended for improving CPX, we’ve made use of it at the office to collect training data for the AI deck.
The Crazyflie with AIdeck during over WiFI controlled flight.
Improvements
Unfortunately I was a bit short on time and the changes for mDNS discovery never made it it. Because of this there’s no way to “scan” or discover AI decks, so to connect you will need to know the IP or the host name. For now you can retrieve that by connecting to your AI-deck equipped Crazyflie with the CFclient and look at the console tab.
A part from that there’s more improvements to be made, with a better structure for using CPX (more like the CRTP stack with functions) in the library and more examples. There’s also still a few bugs to iron out, for example there’s still the improved FPS and WiFi throughput issues.
IMAV 2022
Next week from 13th to 16th of September Barbara, Kristoffer and Kimberly will be present at the international Micro Aerial Vehicle Conference and Competition (IMAV) hosted by the MAVlab of the TU Delft in the Netherlands. One of the competitions is called the nano quadcopter challenge, where teams will program a Crazyflie + AI deck combo to navigate through an obstacle field, so we are excited to see what solutions will come out of that. If any of you happens to be at the conference/competition, drop by our table to say hello!
you had probably seen me from the last blog post when I first arrived. I spent this summer working here in Malmö and I can definitely say that it was one great, educative and fun experience. During the last three months I have been in Bitcraze, I was given the chance to work and develop applications and demos on the robotics subject I am most interested in, drone collaboration.
Centralized Swarm with Multiple Flying Copter
I initially started looking into the implemented swarm demo which had 7 drones charging wirelessly in 7 charging decks and one of them flying by executing a spiral trajectory until it has a low battery and another one takes its place. The original swarm demo was shown at several conferences before the pandemic hit, but my plan was to improve it by adding more quadcopters flying simultaneously. The biggest problem was the collisions and ground effect happening between them. In order to solve that I was based on this paper and the optimization engine OpEn. I solved the problem of all drones starting from a point and going to a final one without colliding and covering the minimum distance by transferring these constraints into a cost function of an optimization problem assuming a simplified model for the quadcopter. Its output is waypoints for each quadcopter to pass from. These waypoints are transformed into a trajectory(piecewise polynomial) by a custom trajectory generator based on linear algebra.
In this way, I made it possible to execute non-colliding trajectories for 4 quadcopters, upload and execute them. While executing the first trajectory, the next one was being calculated and uploaded assuming the goal of the previous one as starting point. In this way, I managed to have 4 Crazyflies flying simultaneously and landing when their battery was running out and the fully charged ones were taking their place. This mechanism with some modifications can be used as a path planner or a standalone trajectory generator from a future user by feeding it waypoints and time durations for each waypoint segment. You can find the source code here.
Decentralized Swarm with Multiple Flying Copters
The aforementioned setup seemed to work pretty well but there was always the need of having a central PC monitoring and taking decisions for the whole swarm. So we wanted to move the architecture to a decentralized one, of which Kristoffer did some preliminary work shown at BAMdays last year. This was made possible by utilizing the onboard peer-to-peer protocol (P2P) in collaboration with the onboard collision avoidance algorithm introduced in this PR contributed by James Preiss from the University of Southern California. All the Crazyflies share their position and state through the protocol by broadcasting them at a rate of 15 Hz.
Although there were some missed packets, they could avoid each other while flying by updating the collision avoidance algorithm which is taking action between the high level and the action commander by altering its waypoints. The decisions of which drone should take off or land are also taken in a decentralized way. Whenever one copter is about to take off it enters the corresponding state and assigns itself a randomized timeout. During this timeout, if the desired number of airborne copters is achieved it goes back to idle. If not and the timeout occurs it finally take-offs. So, despite there is not an actual common decision, the swarm can be led to simple desired states like keeping the number of the drones flying constant and executing changes between the landed, charged copters and flying ones. You can find the source code here.
Token Ring Implementation
After I finished this project and since I had some extra time left I decided to work more in the P2P protocol. The need for having a robust way to communicate between the Crazyflies and a way to verify that a packet was indeed sent was obvious. A solution to this problem was offered by Christos Zosimidis and Klaus Kefferpütz from the Cooperative Control Lab in Hochschule Augsburg, namely a token ring implementation. I would really like to thank them for this collaboration and hope for future ones as well.
Specifically, the proposed token ring protocol was implemented in a modified version of the nrf-radio firmware and the Crazyradio. This protocol assumes that each Crazyflie is a node of a network and a token is passed around giving permission to each drone that has it in its possession to broadcast data. So, each time only one Crazyflie broadcasts data which leads to fewer packet collisions and losses. It can also acknowledge that a node has received the data targeting to it and then continues to others. The interface with the protocol is being done by 2 queues (TX and RX) where the user can place data that wants to send and read the RX queue to receive. The moment that this blog post is being written only the static version of it is public in the firmware, which means that the number and the id of the Crazyflies must be defined before execution and in case a copter fails the whole network fails. Although, I am currently working on a dynamic approach that is going to solve these problems
All in all, I had a great time here in Malmo despite the fact that the Swedish summer is much colder than the warm weather I was used to in Greece. I was amazed by the way things in Bitcraze work and how the whole company operates. It was a pleasure being around so creative people and I am happy that I could help even in a small way. Thank you very much for giving me the opportunity to work with you and I hope I will keep on contributing to this amazing project in the future.
There are some nice and exciting improvement in the CF-client that we worked on during the summer months! First of all we worked on a toolbox structure, where every tab can be reconfigured as a toolbox as well, allowing it to be docked to the sides of the window. Secondly we have added a new geometry estimation wizard for Lighthouse systems to support multi base-station estimation. Finally we have added a new tab for PID controller tuning, mainly intended for the Bolt.
New tabs, toolboxes and wizards for the CFclient
Toolboxes in the CFclient
Everyone who used the CFclient has experienced the tabs before. Anytime you want to configure the lighthouse system, setup plotting or look at the parameter states, you switch to the appropriate tab to perform your desired action. This is all fine, but sometimes it can be useful to see the contents of two tabs at the same time, maybe you want to watch the graphing of a log variable at the same time as you change a parameter. This is what the combined tab/toolbox feature adds! Any tab can now be converted into a toolbox that can be docked to the side of the window.
Plotter tab with parameter toolbox
In the example above the plotter is displaying the estimated position of a Crazyflie with a Flow deck, while the parameter window is opened as a toolbox. The “motion.disable” parameter was just set to true and we can see that the kalman estimator gets into trouble when it no longer gets data from the flow deck.
To switch from tab to toolbox mode, go to the View/Toolboxes menu and select the window that you want to show as a toolbox. In a similar way, use the View/Tabs menu to turn it back to a tab.
Even though all tabs can be turned into toolboxes, some of them might still look better as tabs due to their design. We hope to be able to improve the design over time and make them more toolbox friendly, contributions are welcome!
Lighthouse Geometry Estimation Wizard
In a blogpost of almost a half year ago, we presented a new multi base station geometry estimation method that enabled the user to include more than 2 base station for flying a Crazyflie. This heavily increases the flight area covered by the base station V2s, as technically it should be able to handle up to 16!
New geometry estimation dialog
However, up until this summer it has been in experimental mode as we weren’t so sure as how stable this new estimation method is, so the only way to use it was via a script in the Crazyflie python library directly, and not from the CFclient. Since we haven’t heard of anybody having problems with this new experimental feature, we decided to go ahead to make a nice multi base station geometry estimation wizard in the CFclient’s Lighthouse tab.
This wizard can be accessed if you go to the lighthouse tab-> ‘manage geometry’ and press ‘Estimate Geometry’. We had to make it a wizard as this new method requires some extra intermediate steps compared to the previous, to ensure proper scaling, ground plane setting and sweep angle recording. If you are only using 2 base stations this seems like extra effort, where you only had to put the Crazyflie on the ground and push a button, but if you compare flight performance of the two methods, you will see an immediate difference in positioning quality, especially around the edges of your flight area. So it is definitely worth it!
First page of the wizard
We will still provide the “simple” option for those that want to use it, or want to geometry estimate only one base-station, as we don’t have support for that for this new estimator (see this issue). In that case, you will have to install the headless version of opencv separately like ‘pip3 install opencv-python-headless’. We will remove this requirement from the cflib itself for the next release as there are conflicts for users who has installed the non-headless opencv on their system, like for the opencv-viewer of the AI-deck’s wifi streamer for instance.
PID tuning tab
The PID controller tuning tab
And last but not least, we introduced an PID tuning tab in a PR in the CFClient! And of course… also available as a toolbox :) This is maybe not super necessary for the Crazyflie itself, but for anyone working with a custom frame with the Bolt or BigQuad deck this is quite useful. Tuning is much handier with a slider than to adjust each parameter numerically with the parameter tab. Also if you are just interested of what would happen if you would increase the proportional gain of the z-position controller of the crazyflie, this would be fun to try as well… but of course at your own risk!
If you are happy with your tuned PID values, there is the “Persist Values” button which will store the parameters in the EEPROM memory of the Crazyflie/Bolt, which means that these values will persist even after restarting the platform. This can be cleared with the ‘Clear persisted values’ button and you can retrieve the original firmware-hardcoded default values with ‘Default Values’ button. Please check out this blogpost to learn more about persistent parameters.
Try it out for yourself!
This client has not been released yet but you can already go ahead and try these new features out for yourself. Make sure to first install the client from source, and then install the CFlib from source, as an update of both is necessary. Also update the crazyflie-firmware to the latest development branch via these instructions, especially if you want to try out the new LH geometry wizard.
And of course, don’t forget to give us feedback on discussions.bitcraze.io or to make an issue on the cfclient, cflib or crazyflie-firmware github repositories if you are hitting a bug on your machine and you know pretty precisely where it comes from.
Now it is time to give a little update about the ongoing ROS2 related projects. About a month ago we gave you an heads-up about the Summer ROS2 project I was working on, and even though the end goal hasn’t been reached yet, enough has happened in the mean time to write a blogpost about it!
Crazyflie Navigation
Last time showed mostly mapping of a single room, so currently I’m trying to map a bigger portion of the office. This was initially more difficult then initially anticipated, since it worked quite well in simulation, but in real life the multi-ranger deck saw obstacles that weren’t there. Later we found out that was due to this year old issue of the multi-ranger’s driver incapability to handle out-of-range measurements properly (see this ongoing PR). With that, larger scale mapping starts to become possible, which you can see here with the simple mapper node:
If you look at the video until the end, you can notice that the map starts to diverge a bit since the position + orientation is solely based on the flow deck and gyroscopes , which is a big reason to get the SLAM toolbox to work with the multi-ranger. However, it is difficult to combine it with such a sparse ‘Lidar’ , so while that still requires some tuning, I’ve taken this opportunity to see how far I get with the non-slam mapping and the NAV2 package!
As you see from the video, the Crazyflie until the second hallway. Afterwards it was commanded to fly back based on a NAV2 waypoint in RViz2. In the beginning it seemed to do quite well, but around the door of the last room, the Crazyflie got into a bit of trouble. The doorway entrance is already as small as it is, and around that moment is also when the mapping started to diverge, the new map covered the old map, blocking the original pathway back into the room. But still, it came pretty close!
The diverging of the map is currently the blocker for larger office navigation, so it would be nice to get some better localization to work so that the map is not constantly changed due to the divergence of position estimates, but I’m pretty hopeful I’ll be able to figure that out in the next few weeks.
Crazyflie ROS2 node with CrazySwarm2
Based on the poll we set out in the last blogpost, it seemed that many of you were mostly positive for work towards a ROS2 node for the Crazyflie! As some of you know, the Crazyswarm project, that many of you already use for your research, is currently being ported to ROS2 with efforts of Wolfgang Hönig’s IMRCLab with the Crazyswarm2 project. Instead of in parallel creating separate ROS2 nodes and just to add to the confusion for the community, we have decided with Wolfgang to place all of the ROS2 related development into Crazyswarm2. The name of the project will be the same out of historical reasons, but since this is meant to be the standard Crazyflie ROS2 package, the names of each nodes will be more generic upon official release in the future.
To this end, we’ve pushed a cflib python version of the crazyflie ros2 node called crazyflie_server_py, a bit based on my hackish efforts of the crazyflie_ros2_experimental version, such that the users will have a choice of which communication backend to use for the Crazyflie. For now the node simply creates services for each individual Crazyflie and the entire swarm for take_off, land and go_to commands. Next up are logging and parameter handling, positioning support and broadcasting implementation for the CFlib, so please keep an eye on this ticket to see the process.
So hopefully, once the summer project has been completed, I can start porting the navigation capabilities into the the Crazyswarm2 repository with a nice tutorial :)
ROScon talk
As mentioned in a previous blogpost, we’ll actually be talking about the Crazyflie ROS2 efforts at ROScon 2022 in Kyoto in collaboration with Wolfgang. You can find the talk here in the ROScon program, so hopefully I’ll see you at the talk or the week after at IROS!
Advancements in technology have made quadrotor drones more accessible and easy to integrate into a wide variety of applications. Compared to traditional fixed-wing aircraft, quadrotors are more flexible to design and more suitable for motioning, such as statically hovering. Some examples of quadrotor applications include photographers using mounting cameras to take bird’s eye view images, and delivery companies using them to deliver packages. However, while being more versatile than other aerial platforms, quadrotors are still limited in their capability due to many factors.
First, quadrotors are limited by their lift capacity, i.e., strength. For example, a Crazyflie 2.1 is able to fly and carry a light payload such as an AI deck, but it is unable to carry a GoPro camera. A lifter quadrotor that is equipped with more powerful components can transport heavier payload but also consumes more energy and requires additional free space to operate. The difference in the strength of individual quadrotors creates a dilemma in choosing which drone components are better suited for a task.
Second, a traditional quadrotor’s motion in translation is coupled with its roll and pitch. Let’s take a closer look at Crazyflie 2.1, which utilizes a traditional quadrotor design. Its four motors are oriented in the same direction – along the positive z-axis of the drone frame, which makes it impossible to move horizontally without tilting. While such control policies that convert the desired motion direction into tilting angles are well studied, proven to work, and implemented on a variety of platforms [1][2], if, for instance, we want to stack a glass filled with milk on top of a quadrotor and send it from the kitchen to the bedroom, we should still expect milk stains on the floor. This lack of independent control for rotation and translation is another primary reason why multi-rotor drones lack versatility.
These versatility problems are caused by the hardware of a multi-rotor drone designed specifically to deal with a certain set of tasks. If we push the boundary of these preset tasks, the requirements on the strength and controllability of the multi-rotor drone will eventually be impossible to satisfy. However, there is one inspiration we take from nature to improve the versatility in the strength of multi-rotor drones – modularity! Ants are weak individual insects that are not versatile enough to deal with complex tasks. However, when a group of ants needs to cross natural boundaries, they will swarm together to build capable structures like bridges and boats. In our previous work, ModQuad [3], we created modules that can fly by themselves and lift light payloads. As more ModQuad modules assemble together into larger structures, they can provide an increasing amount of lift force. The system shows that we can combine weak modules with improving the versatility of the structure’s carrying weight. To carry a small payload like a pin-hole camera, a single module is able to accomplish the task. If we want to lift a heavier object, we only need to assemble multiple modules together up to the required lift.
Improving controllability
On a traditional quadrotor, each propeller is oriented vertically. This means the device is unable to generate force in the horizontal direction. By attaching modules side by side in a ModQuad structure, we are aligning more rotors in parallel, which still does not contribute to the horizontal force the structure can generate. That is how we came up with the idea of H-ModQuad — we would like to have a versatile multi-rotor drone that is able to move in an arbitrary direction at an arbitrary attitude. By tilting the rotors of quadrotor modules and docking different types of modules together, we obtain a structure whose rotors are not pointing in the same direction, some of which are able to generate a force along the horizontal direction.
H-ModQuad Design
H-ModQuad has two major characteristics: modularity and heterogeneity, which can be indicated by the “Mod” and “H-” in the name. Modularity means that the vehicle (we call a structure) is composed of multiple smaller modules which are able to fly by themselves. Heterogeneity means that we can have modules of different types in a structure.
As mentioned before, insects like ants utilize modularity to enhance the group’s versatility. Aside from a large number of individuals in a swarm that can adapt to the different scales of the task requirement, the individuals in a colony specializing in different tasks are of different types, such as the queen, the female workers, and the males. The differentiation of the types in a hive helps the group adapt to tasks of different physical properties. We take this inspiration to develop two types of modules.
In our related papers [4][5], we introduced two types of modules which are R-modules and T-modules.
Fig 2. Major components of an H-ModQuad “T-module” we are using in our project. We use Bitcraze Crazyflie Bolt as the central control board.
An example T-module is shown in the figure above. As shown in the image, the rotors in a T-module are tilted around its arm connected with the central board. Each pair of diagonal rotors are tilted in the opposite direction, and each pair of adjacent rotors are either tilting in the same direction or in the opposite direction. We arrange the tilting of the rotors so that all the propellers generate the same thrust force, making the structure torque-balanced. The advantage of the T-module is that it allows the generation of more torque around the vertical axis. One single module can also generate forces in all horizontal directions.
An R-module has all its propellers oriented in the same direction that is not on the z-axis of the module. In this configuration, when assembling multiple modules together, rotors from different modules will point in different directions in the overall structure. The picture below shows a fully-actuated structure composed of R-modules. The advantage of R-modules is that the rotor thrusts inside a module are all in the same direction, which is more efficient when hovering.
Structure 1: Composed of four types of R-modules.
Depending on what types of modules we choose and how we arrange those modules, the assembled structure can obtain different actuation capabilities. Structure 1 is composed of four R-modules, which is able to translate in horizontal directions efficiently without tilting. The picture in the intro shows a structure composed of four T-modules of two types. It can hover while maintaining a tilting angle of up to 40 degrees.
Control and implementation
We implemented our new geometric controller for H-ModQuad structures based on Crazyflie Firmware on Crazyflie Bolt control boards. Specifically, aside from tuning the PID parameters, we have to change the power_distribution.c and controller_mellinger.c so that the code conforms to the structure model. In addition, we create a new module that embeds the desired states along predefined trajectories in the firmware. When we send a timestamp to a selected trajectory, the module retrieves and then sends the full desired state to the Mellinger Controller to process. All modifications we make on the firmware so that the drone works the way we want can be found at our github repository. We also recommend using the modified crazyflie_ros to establish communication between the base station and the drone.
Videos
Challenges and Conclusion
Different from the original Crazyflie 2.x, Bolt allows the usage of brushless motors, which are much more powerful. We had to design a frame using carbon fiber rods and 3-D printed connecting parts so that the chassis is sturdy enough to hold the control board, the ESC, and the motors. It takes some time to find the sweet spot of the combination of the motor model, propeller size, batteries, and so on. Communicating with four modules at the same time is also causing some problems for us. The now-archived ROS library, crazyflie_ros, sometimes loses random packages when working with multiple Crazyflie drones, leading to the stuttering behavior of the structure in flight. That is one of the reasons why we decided to migrate our code base to the new Crazyswarm library instead. The success of our design, implementation, and experiments with the H-ModQuads is proof of work that we are indeed able to use modularity to improve the versatility of multi-rotor flying vehicles. For the next step, we are planning to integrate tool modules into the H-ModQuads to show how we can further increase the versatility of the drones such that they can deal with real-world tasks.
Reference
[1] D. Mellinger and V. Kumar, “Minimum snap trajectory generation and control for quadrotors,” in 2011 IEEE International Conference on Robotics and Automation, 2011, pp. 2520–2525.
[2] T. Lee, M. Leok, and N. H. McClamroch, “Geometric tracking control of a quadrotor uav on se(3),” in 49th IEEE Conference on Decision and Control (CDC), 2010, pp. 5420–5425.
[3] D. Saldaña, B. Gabrich, G. Li, M. Yim and V. Kumar, “ModQuad: The Flying Modular Structure that Self-Assembles in Midair,” 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 691-698, doi: 10.1109/ICRA.2018.8461014.
[4] J. Xu, D. S. D’Antonio, and D. Saldaña, “Modular multi-rotors: From quadrotors to fully-actuated aerial vehicles,” arXiv preprint arXiv:2202.00788, 2022.
[5] J. Xu, D. S. D’Antonio and D. Saldaña, “H-ModQuad: Modular Multi-Rotors with 4, 5, and 6 Controllable DOF,” 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 190-196, doi: 10.1109/ICRA48506.2021.9561016.
