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.
In the first years that I started at Bitcraze I’ve been focused mostly on embedded development and algorithmic design like the app layer, controllers and estimators and such, however recently I started to be quite interested in the robotic integration between the Crazyflie and other (open-source) projects and users. This means that I’ll be dwelling more often in the space between Bitcraze and the community, which is something that I do really enjoy I noticed during the Grand Tour. It also initiated my work with simulators which I think would be very useful for the community too. The summer fun project that I’ve been now working on is to integrate the Crazyflie with ROS2 to integrate standard navigational packages, which will be the topic of this blogpost!
ROS2 Crazyflie Node
So first I worked on the ROS2 node that actually communicates with the Crazyflie directly. I think many of you are familiar with the USC’s CrazySwarm project, of which the ROS2 variant, CrazySwarm2, is already available for most functionalities. Even though the name says CrazySwarm, this can be very easily used for only one Crazyflie too. The CrazySwam2 is currently under more development by the IMRClab of TU Berlin, but please take a look if you want to give it a go!
For now while Crazyswarm2 is still under development, I used the Bitcraze Crazyflie python library to make a more hackish node that just publishes exactly the information I want. I am focusing on the scenario with the STEM ranging bundle, aka the Crazyflie + Flowdeck (optical flow + distance sensor) + Multi-ranger (5 x distance sensors) combo, where the node logs the multi-ranger data and the odometry from the Flowdeck with the Crazyradio and outputs that into necessary /scan and /odom topics. Moreover, it also outputs several tf2 transforms that makes it possible to either visualize it in RVIZ and/or connect it to any other packages and it should react to incoming twist messages as well.
Development with a Simulator
And of course… I went in head first and connected it directly with the SLAM toolbox. I have worked with ROS1 in the past, but I had my first experience working with that package in the course: Build Mobile Robots with ROS2 (by Weekly Robotic Newsletter’s Mat Sadowski), so I couldn’t wait to try it on a real platform like the Crazyflie. However, tuning this was of course more work than I thought, as the map that I got out of it first was mostly a sparse collection of dots. Of course the SLAM toolbox is meant for lidars and not something that provided sparse range distances like the Multiranger. Then I decided to take one or two steps back, and first connect a simulator to make tuning a bit easier.
Luckily, I’ve already started to look at simulators, and was quite far in the Webots integration of the Crazyflie. Actually… Webots’ next release (2022b) will contain a Crazyflie as standard! Once it is out, I’ll write a blogpost about that separately :). As luck has it, Webots also has good ROS2 integration as well, and even won the ‘Best ROS Software’ award by The Construct’s ROS awards! Another reason is that I wanted to try out a different simulator for ROS2 this time to complement what I’ve learned in the ROS2 course I mentioned earlier.
So I used the webots driver node to write a simulated Crazyflie that should output the same information as the real Crazyflie node, so that I can easily hack around and try out different things without constantly disturbing my cats from their slumber :). Anyway, I won’t go into to the simulator too much and save that for another blogpost!
Simple Mapping
I decided to also take another additional step before going full SLAM, which is to make a simple mapper node first! This takes the estimated state estimate of the Crazyflie and the Multiranger’s range values and it creates an occupancy grid type map of it. I do have to give kudos to the Marcus’ cflib Pointcloud script and Webot’s simple mapper example, as I did look at them for some reference. But still with the examples, integration and connecting the dots together is quite some work. Luckily I had the simulator to try things out with!
So first I put the Crazyflie in an apartment simulator, flew around and see if any decent maps comes out of it and it seemed it did! Of course, the simulated Crazyflie’s ‘odometry’ comes from near perfect position estimate, so I didn’t expect any problems there (and in such a situation you would actually not really need the localization part of SLAM). This still needs some improvements to be done, like now range measurements that don’t see anything are excluded from drawing, but still it was pretty cool to map the virtual environment.
So it was off to try it out on a real crazyflie. In one of our meeting rooms, I had one Crazyflie take off, let it turn around with a twist message in a /cmd_vel topic and made a map of the room I was currently in. The effect of the 4 range sensors rotating around and creating a map in one go, makes me think of these retro video transitions. And the odometry drift does not seem as bad for it to be possible, but I haven’t mapped our entire office yet so that might be different!
What’s next?
So I’m not stopping here for sure, I want to extend this functionality further and for sure get it to work with the SLAM_toolbox properly! But if the simple mapper already can produce such quality, I’m pretty sure that this can be done in one way or the other. What I could also do, is first generate a simple map and already have a go at the NAV2 package with that one… there are many roads to Rome here!
Currently I’m doing my work on my personal Github account in the crazyflie_ros2_experimental repository. Everything is still very much in development, hackish and quite specific for one use case but that is expected to change once things are working better, so please check the planning in the project’s readme. In the mean time, you can indicate to us in this vote if this is an interesting direction for us to go towards. Not that it will stop me from continuing this project since it is too much fun, but it is always good to know if certain efforts are appreciated!
Last week we had the first ever Bitcraze DEV meeting! With about 10 participants, we covered a range of topics. The meeting was mostly focused around how to handle support and what the DEV meetings should be about. We also had a chance to get some feedback, and one of the points was sharing a bit more what we’re currently working on and what we might work on in the future. So in the light of that, this blogpost is about CPX (the Crazyflie Packet eXchange) protocol. We’ve mentioned CPX before (1, 2), but with this blogpost I want to share the current status and some thoughts on why we need something new.
As summer is approaching and things are winding down, I’m talking the opportunity to get back to the AI deck and CPX. The AI deck was officially released out of early access last month, but there’s still more work to be done with porting examples, adding some more functionality and increasing stability and performance.
For the AI deck we’re only supplying examples, there’s no functionality that will be used with the platform (except for the WiFi connection maybe). This is in contrast to for instance the Flow deck, where there’s a specified functionality the user can use and that should work. So in order to move forward I came up with a little demo that I want to get working during the summer. The goal is to make an application where I can fly around the Crazyflie with the keyboard and get a video stream back. To achieve this I’m using the Flow deck together with the AI deck and using WiFi for both CPX and CRTP (to send command and to get images and logging).
Why we need something new
I’ve written a post about CPX in the past (link) where I detailed the issues we are trying to solve. But in short we needed was a protocol that …
… could be routed though intermediaries to reach it’s destination
… could handle high transfer rates with large amounts of data as well as small messages
… could handle different memory budgets
… doesn’t drop data along the way if some parts of the system is loaded
As the Crazyflie echo system grows and becomes more complex we need new tools to work with it. When CRTP was implemented many years ago, the complexity we have today wasn’t something we could imagine. The Crazyflie had the only MCU and the hardware on the decks were used directly from it. Now we have multiple decks with more complex systems on them: AI deck (2 MCUs), Active marker deck (1 MCU) and the Lighthouse deck (1 FPGA). Looking forward these more complex decks might increase in the future. With more and more functionality in the Crazyflie and resources occupied, like DMA channels and pins, some functionality might need to move further out onto the decks.
For each deck new protocols are implemented and specific code is needed in the Crazyflie to handle it. Some things also become complex, like getting printouts from the different MCUs on the decks. So for the AI deck we wanted to test something new and more generic to see if it would be something we could use more in the future to talk directly to different MCUs in the system.
Will CPX replace CRTP? Probably not. We’re not sure what solution we will land in, but I think CPX is a good step in the right direction.
Current status
Back to my little demo. To reach the goal there’s a few things which needs to be fixed:
crazyflie-firmware/#1065: When starting to run CRTP over CPX (via WiFi) I’ve noticed that the UART2 driver was too slow, loading the system too heavily and creating problems down the line. So this is being worked on, and at the same time the old SYS-link over UART2 implementation is being moved to CPX instead.
aideck-esp-firmware/#12: We’ve had reports of intermittent performance issues for WiFi, which is also effecting.
Aside from the issues there’s also a few other features that are being added:
CRTP over CPX: Since I already have a connection for the images I also want to use this for controlling the Crazyflie. The latency is too high for controlling roll/pitch/yaw in real-time, but in my case I have the Flow deck for position control
CPX over CRTP: Although not part of the demo, this is interesting to look at for the future. One example is that right now we have an implementation where the Crazyflie firmware has a special implementation for the WiFi credentials. If we would like to set it from the ground we would first have to do CRTP to the Crazyflie, re-package it and then send it via CPX to the ESP32 on the AI deck. Instead I would like to send it via CPX directly from the ground, saving us extra work and complexity in the Crazyflie
Using Zeroconf/mDNS for finding AI decks: With this changes it will be possible to connect to the Crazyflie via the client, so we need a way to find the AI decks. For this Zeroconf/mDNS has been added, so AI decks will be automatically discovered on the local network.
The current status can be seen in the following draft PRs: crazyflie-firmware/#1068 and crazyflie-lib-python/#342. Note that until these are real PRs (not draft) they are not useful, so don’t try to use them yet.
Our Ultra Wide Band (UWB) based positioning system, the Loco Positioning System, has been around for a long time and is still going strong! In this post we will tell you a bit about how it works (for those that don’t know about it yet), what research that is on-going in the field and new developments.
Crazyflie with Loco deck
Basics
UWB is using high frequency, low power, wide band radio where one of the most important properties is that it is possible to detect when a packet is received with very high accuracy. Combining this with very high frequency clocks, opens up the possibility to measure the time it takes for a radio packet to travel from a transmitter to a receiver. Since radio waves propagates with the speed of light in air we can convert the time into distance, and this is the basic idea in UWB positioning.
Not only is it possible to measure the timing of transmissions, the packets can also contain data, like in other radio standards. This property is extensively used to include time stamps of when a packet is sent, and also for instance the time stamp of when the transmitter received other packets or the position of an anchor.
This sounds pretty straight forward, but there are (of course) some complications. We will mention some of them but not go into the details.
Reflections – radio waves bounce around on walls and objects. Luckily, the nature of UWB actually uses this to its advantage and works better indoors than out side.
The clocks in the transmitter and receiver are not synchronized – the Time Of Flight can unfortunately not simply be measured by subtracting reception time from transmission time as the time stamps originate from two different clocks. The problem can be solved by sending some more packets back and forth though.
Packet collisions – two transmitters can not send at the same time, one or both packets will be lost. Transmissions must be scheduled or packet loss must be handled.
Obstacles – obstacles between the transmitter and receiver changes the transmission time.
Antennas – the propagation time through the antenna is substantial and changes depending on the angle to the transmitter/receiver.
Radio interference – other radio sources may interfere with the UWB radio signals and add noise or packet loss.
Modes
The Loco Positioning System can run in two fundamentally different modes: Two Way Ranging (TWR) and Time Difference of Arrival (TDoA).
Two Way Ranging (TWR)
In TWR the Crazyflie measures the distance to one anchor at a time, over and over again. Each measurement in initiated by the Crazyflie and requires 4 messages to be sent between the Crazyflie and the anchor, two request-response pairs. The position is estimated by pushing the measured distances into the kalman estimator.
This mode only supports one Crazyflie, but has the advantage of being very robust and also works pretty well some distance outside the system.
Time Difference of Arrival (TDoA)
In TDoA the setup is different, the anchors are transmitting packets while the Crazyflie is passively listening to the traffic. From the received information it is unfortunately not possible to measure the distance to the anchors, but what we can get is the difference in distance to two anchors. For example, we might know that we are 0.54 meters closer to anchor 3 than anchor 6, or similar. It is possible to calculate the position from this information and similarly to TWR the measurements are pushed to the kalman estimator for further processing.
This mode supports unlimited numbers of Crazyflies (swarms) but is less robust compared to TWR, especially outside the system. TDoA is similar to how GPS works.
Research
There are many researchers that use the Loco System, some use it as a positioning system and investigate topics like path planning or similar, while some others are looking at different questions related to the UWB positioning itself. We will not try to mention everyone, we probably only know of a small fraction of what is going on (please tell us!), but would like to point out two areas of research.
The first is related to improving the estimated position by handling measurement errors and the environment in a better way. Examples of this is to compensate for differences in reception angle or handling of obstacles in the space. We would like to mention Wenda Zhao’s work at the Dynamic Systems Lab, University of Toronto. He has contributed the robust TDoA implementation in the kalman estimator (blog post) as well as a public TDoA data set.
The second is inter drone ranging, that is measuring the distance between drones as an addition to, or instead of drone-to-anchor measurements. Examples in this are are the work by Dr Feng Shan at School of Computer Science and Engineering Southeast University, China (blog post) and professor Klaus Kefferpütz, Hochschule Augsburg, work on “Crazyflie quadcopter in decentralized swarming” as presented on the BAM days last year.
Experimental functionality
Even though there has not been a lot of code committed lately in our repositories related to the Loco Positioning System, it has been simmering in the background. We would like to mention what is cooking in the pots and some of the stuff that has been discussed or tested.
System size
An 8 anchor Loco Positioning System can cover a flight space of around 8×8 meters, but from time to time we get the question of larger systems. TDoA3 was designed with this in mind and supports up to 255 anchors, which in theory would make it possible to build larger systems. This functionality was implemented 4 years ago but we never really tested it(!). Finally we collected all anchors in the lab an set up 20 anchors in the same system, and it worked! This should make it possible to extend systems to at least 15×15 meters, but maybe even more with some clever radio cell planing.
Another possibility to enlarge a system is to tweak the radio settings to make them reach longer. There is a “Longer range” mode in TDoA3 that lowers the bit rate, but again it has not really been verified. This was also tested in the latest Loco frenzy and with some minor modifications it worked the way we hoped, with 20 anchors! The tests mainly verified that the anchors play nicely together, and we are not sure about the maximum range (to be tested) but we believe distances of up to 40 meters between anchors is possible. To use this feature you should make sure to use the latest firmware for the Loco Nodes as well as the Crazyflie.
The two features mentioned above should hopefully make it possible to go big and we hope it could be used for shows for instance.
TDoA3 hybrid mode
If one looks at the messages sent in a TDoA system, the anchors are actually doing TWR with each other, while the Crazyflie(s) are just listening to the traffic and that the possibility to extract the position is a nice “side effect”. Now imagine if the Crazyflies were to send some messages from time to time, then they could act as “dynamic” anchors, or do inter-drone ranging with each other. This is something we call TDoA3 hybrid mode.
Currently there is no official implementation of the Hybrid mode, only some experimental hacks. Some researchers have done their own implementations, but we hope, at some point, to generalize the functionality and integrate it into the firmware.
Read more
If you are interested to read more about positioning and the Loco system, you can take a look at the following link list.
Summer is coming and with that vacations, yeay! There will always be someone at the office to help you if you need help, and we will handle shipping through out the summer, but it might take a bit longer than usual.
Earlier this month, ICRA 2022 was in held in Philadelphia and in person this time! Unfortunately we were unable to attend ourselves but quite happy that there were still virtual attendance options available. So I followed quite some presentations and read through papers, trying to find out the latest in Aerial and Swarm robotics and if anybody was able to use the Crazyflie to good use for their research. I even had the opportunity to attend the Exhibition floor with a telepresence robot, which was a lot of fun!
We have covered IROS 2021 end of last year, and we even have started to publish Crazyflie related publications on social media to keep ourselves and the community up to date with any Crazyflie research work. So here we will list the ICRA 2022 papers we have found and write some observations.
Crazy Platforms
What I really noticed this year is that the Crazyflie has been used in more unconventional configurations and new platforms! IROS 2021 ready amazed us by a solar-powered Crazyflie and the 4 times Crazyflie combined quadcopter (which continued this conference by UCLA in (2). But we haven’t seen yet that a Crazyflie can jump! The PogoDrone by the Swarmslab of Lehigh university turned the Crazyflie into an autonomous jumping pogo stick (5)! Moreover, wheels were added by the Institute For Systems and Robotics (TU Lisbon) for increasing the flight/autonomy durability (7).
We also noticed 3 ICRA 2022 papers with Bolt-powered platforms, which is a huge increase compared to IROS 2021 which only had 1 Bolt entry. The MAVlab of the TU Delft compared the Crazyflie against a Bolt-powered Flapper-drone for flying against wind (see the presentation of Flapperdrone in our last MiniBam). Moreover, remember that saw the Science Robotics paper using a Crazyflie board for a dual wing rotating platform. The Engineering product development of SUTD took a similar design to the next level, building a single controllable rotating wing with a Bolt platform (3). Two of these can even work together cooperatively and fly stability, so it is no wonder that they won the ICRA 2022 Outstanding Dynamics and Control Paper Award.
List of ICRA 2022 Papers featuring the Crazyflie and Bolt
Here is a list of all the Crazyflie/Bolt papers featured in ICRA 2022 but let us know if we are missing any (⚡: Bolt, 🐝: Crazyflie). Mind that only Robotic and Automation Letter entries have been officially published on IEEE Xplore already, so from the proceeding papers I tried to share the ArXiv paper if available.
⚡ ‘Passive Wall Tracking for a Rotorcraft with Tilted and Ducted Propellers using Proximity Effects’ Ding et al. from City University of Hong Kong & Massachusetts Institute of Technology
⚡x2 ‘Cooperative Modular Single Actuator Monocopters Capable of Controlled Passive Separation’, Cai et al. from Singapore University of Technology & Design
🐝x 6 ‘Formation-containment tracking and scaling for multiple quadcopters with an application to choke-point navigation’, Su et al. from The University of Manchester.
11. 🐝x 6 ‘Nearest-Neighbor-Based Collision Avoidance for Quadrotors Via Reinforcement Learning’, Ourari et al. from TU Darmstadt – ArXiv
12. 🐝x 6 ‘Safe multi-agent motion planning via filtered reinforcement learning’ Vinod et al. from Mitsubishi Electric Research Laboratories – IEEEXplore page
13. 🐝 ‘Event-Triggered Tracking Control Scheme for Quadrotors with External Disturbances: Theory and Validations’, Goa et al. from University of Shanghai for Science and Technology – Outstanding Coordination / Mechanisms & Design / Locomotion / Navigation Award Finalists – IEEEXPlore page
14. 🐝 ‘Watch and Learn: Learning to control feedback linearizable systems from expert demonstrations’, Sultangazin et al. from University of California – IEEEXplore page 15. ‘KoopNet: Joint Learning of Koopman Bilinear Models and Function Dictionaries with Application to Quadrotor Trajectory Tracking’, Folkestad et al. from Caltech – IEEEXplore page
Other Announcements: Bolt 1.1 and Dev meeting
Bolt 1.1
The Bolt is now back in stock and with two small updates making it the Bolt 1.1. Here are the changes listed:
The board thickness has been reduced from 1.6mm to 1.0mm to save some weight, roughly 2 grams. This is handy for the slimmest and most lightweight designs.
Motor signal output M4 has been moved from PB9 to PB10 to be able to support the DSHOT motor signal protocol in the future.
Other then that it is fully backwards compatible but make sure to use a recent enough firmware (2022.03) that has the Bolt 1.1 device support added.
Time and Date for Dev Meeting
In this blogpost we noted that we wanted to organize our first Developer meeting before the summer break. From this poll we saw that most of you that want to attend are currently located in Asia and Australia, so that is why this time we want to organize the meeting at:
13:00 CEST (Sweden time) on Wednesday 22th of June.
The topic will be about our new support platform and support handling in general, so I’m hoping for some fruitful discussions about that. Keep an eye on this discussion thread for any details for joining.
This week, we welcome Airi Lampinen from Stockholm University, to talk about the Crazyflie competition she’s organizing in Stockholm.
Welcome to our one-of-a-kind hackathon with Bitcraze’s Crazyflie in Stockholm, Sweden, on June 15-17, 2022! If you are curious about how technology and humans may play together, enthusiastic about the Crazyflie, or eager to learn how to use the Crazyflie, this event is for you.
Image credit: Paul Bechat, ETH Zurich
What, where, when?The Inaugural Challenge at the Digital Futures Drone Arena takes place on June 15-17, 2022 at KTH’s Reactor Hall – a dismantled nuclear reactor hall – which – especially if you haven’t been to this cool space before – makes attending the event worthwhile in its own right. In 2016, the reactor hall was used to film the music video for Alan Walker’s song Faded (Restrung).
Who can join? Anyone irrespective of age, profession and past experience with drones is welcome to participate. We welcome up to 10 teams of 2-4 people. We provide all the necessary drone hardware to the participants. We use the Crazyflie 2.1 and the Lighthouse positioning system. All that a team needs to bring along is a computer. Registration is open, with a final deadline on June 5 – we encourage those interested to sign up as soon as possible to secure their spot!
Program & prizes? On the first day of the hackathon, we will run short tutorials for those with no or little previous drone experience. The teams will then have access to the Reactor Hall to work on the challenge and conduct trial runs with their drone – we offer long hours but each team is free to choose how much they want to work. (The goal here is to have a good time!) The competition itself takes place on the third and final day. We’ve got exciting prizes for the most successful teams!
We have also some Bitcraze news to share with you:
Last wednesday, we had our very first mini BAM, and it led to 2 hours of interesting talks and exciting discissions ! If you’ve missed it, you can find the recordings in your Youtube Channel: here for Flapper Drones’ presentation, and here for Collmot‘s talk. We plan on having at least one another mini BAM before the end of the year, so stay tuned if you’re interested in those events.
Finally, as I talked about in this blogpost, we are looking for a new team mate to add to the Bitcraze crew. You’re interested? Check out our jobs page if you want to learn more !
We have worked hard last week to get a new fresh release out before the summer months are on our doorstep. Not only that we would like to make sure that important bugs are fixed before some of us go on our holiday, but also to be able to display our new AI deck features! Here is an overview of what has been changed
AI deck over air flashing
As you can probably see in the release notes of both the python libraries and the firmware, most of our changes are focused on making it possible to develop for the AI deck without using a programmer all the time. If the STM and NRF firmware of the Crazyflie is fully updated, and the ESP firmware on the AI deck, it should now be possible to flash an AI deck example binary with a Crazyradio! For older versions of the AI deck 1.X (Rev A to C) it is unfortunately still necessary to use the JTAG programmer one last time to flash a bootloader on the GAP8, but after that it should not be needed anymore.
In the light of the work we have done for the AI deck, we also have started to implement a new, inter MCU protocol called the Crazyflie Packet Exchange. Since with the AI deck, we are adding 2 additional microprocessors to the Crazyflie architecture, it was crucial to handle the communication between all platforms and communication channels properly. Currently the functionality is mostly enabled to tailor Wifi streaming and console printouts for the AI deck, but it is meant to be a generic protocol which in the future, should be able to handle more combinations like for instance, command messages through wifi?
For the last part of the Grand tour trip, we had a hackathon with the IMRC lab of TU Berlin and our close collaborator Wolfgang Hönig, in which we managed to convert the PID controller, Mellinger controller and the motor mixing into python bindings, which can be used in the experimental simulator of the Crazyflie.
There is no Pypi release of these, you will need to pull the latest crazyflie-firmware repo and build the bindings with ‘make bindings_python’
Additional fixes
We have some additional fixes to both the python libraries and firmware. For the STM we have updated the STD peripheral library and solved several build issues. For the cfclient, we fixed a lot of issues that were caused by either the latest version of python, as it was more stricter with type definitions, and some issues QT. Moreover, the LED ring headlight functionality has been restored, and the cfbridge.py script, used for the PX4 crazyflie 2.1 tutorial, is re-added, since it suddenly disappeared a few releases ago.
Update and Feedback
Make sure to update your cfclient with ‘pip install cfclient –upgrade’ and to reflash the new stable firmware. For AI deck users, try out our our new tutorial to try out both CPX, the over air flashing and the wifi example. The new AI deck functionalities has been subjected to some limited testing so if there is anything wrong or unclear, please let us know in the forum! The feedback will help the AI deck to become a more stable product for development, so we would be very grateful if you would be able to help out with that.
We recently added improved support for assert information in the client and wanted to take this opportunity to describe some of the features in the console tab of the client that are useful for debugging and profiling.
Example of the console tab
The console tab in the python client is where you can get real time logs from the Crazyflie when connected. Any DEBUG_PRINT() statements in the Crazyflie firmware will popup here and is obviously a simple way of adding debug information to your firmware. The console logs are buffered in the Crazyflie and dumped to the client when you connect, this is why you will see the start up information when connecting to a Crazyflie. If too much information is logged and the buffer is full, you will unfortunately loose some of it but you will be notified by a “<F>” marker in the console window.
On the right side of the console tab window you will find some useful buttons, the first being the “Clear” button that simply clears the console window.
Task dump
The “Task dump” button will print a list with information about the FreeRTOS tasks running in the system, for instance something like this.
The “load” column contains how much of the total time that was spent in each task, since the previous measurement (or boot). To get useful values when performing some task, you probably want to make a dump at the start of your measurement and a second one at the end to get the average during this specific time.
The “Stack left” shows how many bytes of stack that is left for each task, this is the worst recorded number in the period. Stack size is recorded at task switch time which means it is possible that more stack actually was used at some point, but it should give a good indication if a task is running out of stack.
Assert info
Next up is the new “Assert info” button, it will dump assert or crash info to the console. When the STM CPU encounters a hard fault or some other condition that resets the CPU, it will record some basic crash information in a specific part of the RAM. This special RAM is not reset when the STM re-boots and it will automatically be dumped to the console log for investigation during the start up sequence. The “Assert info” button simply dumps the same information again, which may not sound very useful. But in some cases a client may auto-reconnect to a crashed Crazyflie, consume the console log and dispose of it before a human had the opportunity to look at it. In this case you can simply connect the client to the Crazyflie and click the “Assert info” button to get the information again.
Propeller test
The “Propeller test” button runs a automated test of the propellers and measures vibrations in the platform to determine if they are well balanced or not. The result is printed in the console window, like this: (looks like it is time to change one of my propellers!)
The final button is the “Battery test”. It tests if the battery is worn out by spinning the motors and measuring the drop in voltage. A drop in the voltage indicates that the battery probably is bad, but it can also be caused by other sources of extra resistance in the power path, for instance oxide on the battery connector. Use it as an indication only!
Note: Only use this test for the Crazyflie 2.x, not the Bolt or BigQuad.
The result of this test is printed in the console log:
It is possible to add the console log as a tool box at the bottom or one of the sides of the client. In the “View” menu, choose “toolboxes” and click “Console”. A toolbox window with the console log will appear at the bottom of the screen which can be handy as it will be visible even if you switch to another tab.
The Plotter tab with the console as a toolbox
Other debug tools
This post has been focused on the console tab, but there are of course other functionality that is useful when debugging your system. We will end by quickly mention some of them:
If anybody noticed a delay of my response on emails, forum or Github, that might be due to the fact that I was on the road for Bitcraze for the last few weeks! I was invited to give a guest lecture for a course at EPFL, and of recent they have a CO2 reducing policy regarding travel. At Bitcraze we also aim for reducing our environmental impact, so hence the idea came forth to travel to Switzerland and visit our close collaborators that are nearby(ish)… all by train! Internally we dubbed this to be The Grand Tour.
The Itinerary
We kept the itinerary mostly within Switzerland and Germany, although I did pass the Netherlands a few days just to visit family. The full itinerary by train was:
Utrecht (NL) -> Lausanne (SW) -> Zürich (SW) -> Munich (GE) -> Berlin (GE) -> Malmö (SE)
The longest train ride was from Utrecht to Lausanne (9 hours), but all the others were well under 4 hours which was pretty comfortable. The nice thing about being in the train is that it quite easy to work on your laptop (although the wireless network + onboard WiFi was still patchy). Luckily I was able to actually phone in for Bitcraze’s morning meetings so that I wouldn’t miss a thing.
Here are some pictures of the in-between travels, with the views, trains and food. It was all awesome, but if I do have to make a confession… the train rides through Switzerland was the most beautiful of all!
Travelling through Switzerland and Germany
The People
The first two days in Lausanne went quite smoothly. Dario Floreano of the Laboratory of Intelligent Systems (LIS) invited us to give a Crazyflie 101 lecture to the students of the Aerial Robotics course, for which we are very grateful for the opportunity. It was great to do the talk in person this time and visit the EPFL campus, since the last two years I’ve given the same lecture from my own kitchen. I was able to see the students trying to start up the course themselves, and actually got to experience how they would install the Crazyflie framework. Next to my lecture, I was given a very nice tour through the offices, laboratories and work-spaces, where I had the possibility see all the nature inspired drone designs of the LIS-lab. In the meantime I also squeezed in a quick but fun visit with Cyberbotics, the creator of Webots, to discuss our latest efforts for a crazyflie simulator.
After a beautiful train ride towards Zurich, I first met up with the people of the Automatic Control lab (ACL), who made a video about how they handled education with the Crazyflie during the harsher COVID times. Now I got a chance to see the flight room where students are able control their Crazyflie down to the rate attitude controller. Moreover, I was treated to a full workshop, hosted by ETH Zürich’s Integrated Systems Lab (IIS) and Center of Project Based learning (PBL), joined by researchers from ETHZ, University of Bologna and IDSIA (Lugano) working on the PULP platform and/or nano-drones. The workshop consisted of them and us showcasing our current work, future plans and they showed me very impressive demos with both the AIdeck and their own prototypes decks! Complete that with a lunch with one of the best views any campus has to offer, coffee break talks, and you have a very inspiring day.
The third part of the trip took place in Germany! My first stop was near Munich, namely Hochschule Augsburg, where I visited the Cooperative Control Lab lead by Klaus Kefferpütz where we had great discussions about collaborative swarms and state estimators. They showed their lab with demos, and we spoke about positioning systems and how to improve their development experience. They are currently integrating the Bolt with a Raspberry Pi with the latest functionalities we implemented into our firmware, which we can imagine is a very wanted feature by the community! I also had a brief visit at TU Munich as well to visit my friend Sophie Armanini from the eAviation and Sustainable Flight Group, and to my surprise I got to fly with a Crazyflie Bolt fueled Flapper drone!
As my final stop, I visited Wolfgang Hönig from the Intelligent Multi-Robot Coordination Lab (IMRC) at TU Berlin. Here we discussed all about Crazyswarm, simulations and firmware python bindings among many things. Also, we had a successful hackathon where we managed to generate python bindings of the Mellinger & PID controller and the motor mixing. On top of that, we managed to fly with the PID binding in the Webots simulator, which has been on the wish list for a little while now. It was great working together again in person after 1.5 years!
Collection of the tours, the platforms and the people I’ve met!
The Insights
It was great to see all the different ways that our products are used and what matters to the community members were dealing with. I’ve visited labs that tweak the attitude rate controllers, trying to improve the quality of the state estimators, or experiment with the actual mechanics. However, it was clear to see that quite some were controlling the Crazyflies on a higher level of autonomy, either off-board or onboard. This is all spread out over education and research alike, so there is a very wide range of people that are working with the Crazyflie.
There is of course also a huge variety in their approach. Some used our internally development framework with the Cflib and cfclient, and I’ve generated quite some new Github tickets in those respective repositories based on the discussions I had. However, it was interesting to see that many have made their own clients to tailor more to their research and education objectives. Moreover, about half of the users I met used ROS to interact with the Crazyflies. Is it perhaps a sign that we should start to rethink the communication infrastructure and how it all fits together?
There was also quite the difference on how close these users were on our latest changes. It ranged from working on a branch forked 4 years ago to being on the very edge of the commits, which each have their pros and cons. Working on a stable branch that has been proven worthy might be beneficial with education classes, but also makes people miss out on new features like the new lighthouse integration. However, it is not all fine and dandy on the edge of development either, as I have heard of many having issues with the new kbuild intergration, installing the cfclient or our latest efforts of getting the AIdeck out of early access. That is something that these pioneers has to deal every time they merge the new master, so we need to find better ways to make it easier for them as well.
And last but not least, it seems that the simulation we have been working on has generated quite the buzz, as most of whom I spoke to were quite interested in it, or has used a different simulation for their purpose. It was clear that there is not yet a standard simulator for aerial robotics that can fulfill everybody’s requirements in terms of swarming, (vision-based) autonomy or control. Perhaps that is a good reason to promote the simulation work from Fun-Fridays to a regular day project and have some interesting future discussions with the community how to shape this to most of our needs.
The Conclusion
All and all, those were very inspiring 2 weeks of travel for me. Even though physically I was a bit exhausted afterwards, mentally it was very motivating and inspiring! After two of the worst years of the pandemic it was great to talk to people in person and I really feel stronger connections with those I visited than the remote video calls we have done before. It is so important to stay in touch with the community in person, after so long time of absence, as we get a better sense of what the needs are and how people are using the Crazyflie and its ecosystem. The Grand Tour was according to us a great success, and who knows…. perhaps we will do an 2023 edition as well :)
