I already talked about it here and there, but this day finally came: the whole company is in Japan ! Kimberly travelled first, to account for jetlag, meet with some people, and attend ROScon.
It was last week, and she got the opportunity to learn a lot, meet people from the ROS community, and give an exciting talk.
Kimberly’s talk at RosCon (made by Ramón Roche)
Happy to be in Japan (Made by Ramón Roche)
The rest of the company travelled last week with all the equipment needed divided into our suitcases.
Our suitcases at the office, to gather the materials before going
We chose to rent a traditional machiya while there, where we can all stay together and enjoy the life in the center of Kyoto.
Us chilling out in the Bitcraze mansion
Our first day here was to account for jetlag, but we managed to sightsee the amazing sites of Kyoto – and enjoy the most praised Japanese food, much appreciated after a long walk among the Tori gates of the Fushimi Inari shrine.
Us after climbing on top of Mt Inari – with the beautiful path of Tori gates
But it was soon time to start working, and yesterday we worked really hard on setting up everything to have a nice demo at IROS.
After some head scratching, emergency taping and hacking we managed to get the autonomous demo that Marios implemented last summer flying – just before the event hall We got time to explore the Kyoto International Conference Center, a beautiful venue with a Japanese garden and a futuristic look – as imagined in the 70′.
Some views from the Kyoto Conference Center
We invited those of you that are attending IROS to come and see us for a tech meet-up. It’s today and it would be a real nice opportunity for us to finally chat in person with our users ! Since there are a lot of aerial systems talks, we realize it may be difficult to come during the sessions, so the tech meet-up can begin during the break, at 15.40
Next up this week is the safe nanocopter competition. Kimberly will actually deliver the prize for that, we can’t wait to see what this competition will show – and how fun it is to remote-control the Crazyflies that are in the University of Toronto Institute for Aerospace Studies!
Of course, we will share some news on social media – and we will have a blogpost in a few weeks to debrief on the whole trip.
As you’ll understand, maintaining the day-to-day of the company is a little trickier this week, but we still monitor email, github discussions, and are shipping orders. You should just expect a longer time to process those, as we’re too busy – either at the booth or… at karaoke ! (no, there will be no videos of us singing).
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.
Last week we went on a nice trip to Delft, The Netherlands to attend the 22th International Mico Aerial Vehicle Conference and Competition, this time organized by the MAVlab of the TU Delft. Me (Kim), Barbara and Kristoffer went there by train for our CO2 policy, although the Dutch train strikes did made it a bit difficult for us! Luckily we made it all in one piece and we had a great time, so we will tell you about our experiences… with a lot of videos!
First Conference day
For the conference days we were placed in the main aula building, so that everybody could drop by during the coffee breaks, right next to one of our collaborators, Matěj Karásek from Flapper Drones (also see this blog post)! In the big lecture hall paper talks were going on, along with interesting keynote speeches by Yiannis Aloimonos from University of Maryland and Antonio Franchi from TU Twente.
In between the talks and coffee breaks, we took the opportunity to hack around with tiny demos, for which the IMAV competition is a quite a good opportunity. Here you see a video of 4 Crazyflies flying around a Flapperdrone, all platforms are using the lighthouse positioning system.
The Nanoquadcopter challenge
The evening of the first day the first competition was planned, namely the nanoquadcopter challenge! In this challenge the goal was to autonomously fly a Crazyflie with an AIdeck and Flowdeck as far as possible through an obstacle field. 8 teams participated, and although most did offboard processing of the AIdeck’s camera streaming, the PULP team (first place) and Equipe Skyrats (3rd place) did all the processing onboard. The most exciting run was by brave CVAR-UPM team that managed to do pass through 4 gates while avoiding obstacles, for which they won a Special Achievement Award.
During the challenge, Barbara also gave a presentation about the Crazyflie while Kristoffer build up the lighthouse positioning system in the background in a record breaking 5 minutes to show a little demo. After the challenge, there were bites and drinks where we can talk with all the teams participating.
Here there is an overview video of the competition. Also there was an excellent stream during the event if you would like to see all the runs in detail + presentations by the teams, you’ll have have a full 3 yours of content, complemented by exciting commentary of Christophe de Wagter and Guido de Croon from the MAVlab. Thanks to all the teams for participating and giving such a nice show :)
The overview video
The full stream of the nanoquadcopter challenge
The Green House Challenge
On Wednesday, we were brought to Tomato world, which is a special green house for technology development in horticulture. Here is where the Greenhouse challenge, which was the 2nd indoor drone competition took place. The teams had to participate with their drone of choice to navigate through rows of tomato plants and find the sick variant. Unfortunately we could not be up close and personal as with the nanoquadcopter challenge, but yet again there was a great streaming service available so we were able to follow every step of the way, along with some great presentations by Flapper drones and PATS! drones among others. For the later we were actual challenged to an autonomous drone fight! Their PATS-x system is made and detect pest insects that are harmful for green house crops, so they wanted to see if they can catch a Crazyflie. You can see in the video here that they manage to do that, and although the Crazyflie lived, we are pretty sure that a real fly or moth wouldn’t. Luckily it was a friendly match so we all had fun!
PATS versus Crazyflie battle
Here is an overview of the Green house challenge. At the end you can also see a special demo by the PULP team successfully trying out their obstacle avoiding Crazyflie in between the tomato plants. Very impressive!
Last days and final notes
Due to the planned (but later cancelled) train strikes in the Netherlands, the full pack were not able to attend the full event unfortunately. In the end Barbara and I were able to experience the outdoor challenge, where much bigger drones had to carry packages into a large field outdoors. I myself was able to catch the first part of the last conference day, which included a keynote of Richard Bomprey (Royal Veterinary College), whose lab contributed to the mosquito-inspired Crazyflie flight paper published in Science 2 years ago.
We were happy to be at the IMAV this year, which marks as our first conference attendance as Bitcraze after the pandemic. It was quite amazing to see the teams trying to overcome the challenges of these competition, especially with the nanoquadcopter challenge. We would like to thank again Guido de Croon and Christophe de Wagter of the MAVlab for inviting us!
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!
Unmanned Aerial Vehicles (UAVs) have garnered much attention from both researchers and engineers in recent decades. Aerial robots in general are classified into mainly three categories: fixed wings, rotary wings and flapping wings.
Fixed wings are one of the most common aerial vehicles as it has relatively higher power efficiency and payload capacity than other types, thanks to their big and highly customizable wing. But this also leads to a bigger footprint and usually the lack of ability for Vertical Taking Off and Landing (VTOL). Rotary wings generally include helicopter and multirotors (such as quadrotors), and they have recently become increasingly popular in our daily lives. Easily achieving great performance in attitude and position control, rotary wings are widely applied in many fields. Flapping wing robots take inspirations from small flapping insects (such as Harvard Robobee) or birds (Purdue Hummingbird Robot).
Fig: A simple prototype of SAM from SUTD with Crazyflie Bolt.
Monocopters are largely inspired from the falling motion of maple seeds, and they are relatively much simpler to build as compared to its counterparts. They can keep a relative smaller footprint and achieve decent control performance although they are highly underactuated. The Single Actuator Monocopter (SAM) has the ability to VTOL, perform 3D trajectory tracking as well as maintain high hovering efficiency. With those advantages, rapid developments have been made in recent years such as the Foldable Single Actuator Monocopter (F-SAM) and Modular Single Actuator Monocopter (M-SAM) from Engineering Product Development (EPD) of Singapore University of Technology and Design (SUTD).
Taking inspiration from nature – Samara inspired monocopter
A descending samara or maple seed, is able to passively enter auto-rotation motion and stabilize its flight attitude, helping to slow down its descent speed and travel further for better survival of the species. This natural behavior attracts interests from scientists and researchers. With previous studies, we learnt that this passive attitude stability is mainly guaranteed by mass distribution (Center of Mass) and wing geometry (Center of Pressure) as well as the rotation motion.
A maple seed inspired Single Actuator Monocopter (SAM).
The SAM is designed to be very close in its mechanical make-up to its natural sibling, having a large single wing structure and a smaller, denser ‘seed’ structure. A single motor with propeller is installed on the leading edge, parallel to the wing surface. Comparing with flight dynamics of the original maple seed, SAM has extra torques and force caused by the spinning propeller, including a reaction torque and thrust directly from propeller, as well as an extra torque caused by precession motion. As a result, the balance of the combined forces and torques allows SAM to enter a new equilibrium condition while still retaining the passive attitude stability.
Development of monocopters
The research on monocopters can be traced back to a long time ago. Here are some examples of different types of air frame to roughly introduce their developments. An air-frame called Robotic Samara [1] was created in 2010, which has a motor to provide rotational force, a servo to control collective pitch of the wing, a winged body fabricated by carbon fiber, and a lipo battery. In the following year, Samarai MAV [2] was developed by following the mass distribution of a natural maple seed. To achieve the control, a servo is equipped to regulate the wing flap. In 2020, a single actuator monocopter was introduced with a simplified air-frame [3]. The main structure is made by laminated balsa wood while the trailing edge of the wing is made by foam for better mass distribution. By making use of the passive attitude stability, only one actuator is required to control the position in 3D space. Based on which, F-SAM [4] and M-SAM [5] were developed in 2021 and 2022 respectively.
SAM with foldable wing structure (F-SAM).
A Modular SAM (M-SAM) with Crazyflie Bolt
Thanks to its easy implementation and reliable performance, we use the Crazyflie Bolt as the flight controller for M-SAM. Like other robotic systems, the ground station is integrated with motion capture system (position and attitude feedback for both control and ground truth) and a joystick (control reference directly generated by user) is responsible for sending filtered state feedbacks and control references or control signal directly to flight controller. This is realized by employing the Crazyradio PA under the Crazyflie-lib-python environment. Simple modifications from the original firmware were made to map from the control reference to motor command (a customized flight controller).
A diagram shows how Crazyflie Bolts work in M-SAM project.
Another advantage of using Crazyflie Bolt in M-SAM project is its open source swarm library. Under the swarm environment, SAMs can fly in both singular and cooperative configurations. With simple human assistance, two SAMs can be assembled into cooperative configuration by making use of a pair of magnetic connectors. The mid-air separation from cooperative configuration to singular configuration is passively triggered by increasing the rotating speed until the centrifugal force overcomes the magnetic force.
Modular Single Actuator Monocopters (M-SAM), which is able to fly in both singular and cooperative configuration.
Potential applications
What kinds of applications can be achieved with the monocopter aerial robotic platform? On the one hand, many applications are limited by the nature of self-rotation motion. On the other hand, the passive rotating body also offers advantages in some special scenarios. For example, SAM is an ideal platform for LIDAR application, which usually requires the rotating motion to sense the environment around. Besides, thanks to simple mechanical design and cheap manufacturing cost, SAM can be designed for one time use such as light weight air deployment or unknown, dangerous environments.
An example [6] shows the potential applications of a rotating robot with camera.
Reference
[1] Ulrich, Evan R., Darryll J. Pines, and J. Sean Humbert. “From falling to flying: the path to powered flight of a robotic samara nano air vehicle.” Bioinspiration & biomimetics 5, no. 4 (2010): 045009.
[2] Fregene, Kingsley, David Sharp, Cortney Bolden, Jennifer King, Craig Stoneking, and Steve Jameson. “Autonomous guidance and control of a biomimetic single-wing MAV.” In AUVSI Unmanned Systems Conference, pp. 1-12. Arlington, VA: Assoc. for Unmanned Vehicle Systems International, 2011.
[3] Win, Luke Soe Thura, Shane Kyi Hla Win, Danial Sufiyan, Gim Song Soh, and Shaohui Foong. “Achieving efficient controlled flight with a single actuator.” In 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 1625-1631. IEEE, 2020.
[4] Win, Shane Kyi Hla, Luke Soe Thura Win, Danial Sufiyan, and Shaohui Foong. “Design and control of the first foldable single-actuator rotary wing micro aerial vehicle.” Bioinspiration & Biomimetics 16, no. 6 (2021): 066019.
[5] X. Cai, S. K. H. Win, L. S. T. Win, D. Sufiyan and S. Foong, “Cooperative Modular Single Actuator Monocopters Capable of Controlled Passive Separation,” 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 1989-1995, doi: 10.1109/ICRA46639.2022.9812182.
[6] Bai, Songnan, Qingning He, and Pakpong Chirarattananon. “A bioinspired revolving-wing drone with passive attitude stability and efficient hovering flight.” Science Robotics 7, no. 66 (2022): eabg5913.
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.
As the Crazyflie ecosystem expands, more and more novel aerial (but also ground or hybrid) robots are being built with one of the Crazyflie controllers onboard. For recent examples, you can check e.g. the recent blogpost about ICRA 2022.
In this post, I will introduce yet another Crazyflie-Bolt-powered aerial robot, the Flapper Nimble+ from our company Flapper Drones, which unlike other flying robots doesn’t have any propellers but uses flapping wings instead.
The best aerial robot design is…
Small drones, or micro air vehicles, have seen a lot of progress and new developments in the last 20 years. The most widespread design nowadays is a quadcopter, such as the Crazyflie 2.1. But is a quadcopter the ultimate (micro) drone solution? At Flapper Drones, we believe nature might provide even better designs… For some applications at least! 😊
Flying like a bird…
Flapper Drones is a spinoff of the MAVLab of the Delft University of Technology. At the MAVLab, we have been researching bio-inspired flight as part of the DelFly project since 2005. From the beginning, the goal has been to develop a lightweight, mission capable micro air vehicle, the design of which would draw inspiration from nature. Over the years, many such MAV concepts have been designed, built and tested, including the DelFly Micro, the world’s smallest camera-equipped MAV, or the DelFly Explorer, the first autonomous flapping-wing MAV equipped with a stereovision system. All these designs were propelled by a pair of flapping wings, while being controlled (and passively stabilized) by a tail such as birds or men-made airplanes.
… or an insect
The latest design, the DelFly Nimble is insect-inspired instead. What does that mean? The Nimble has no tail, which would provide the damping needed for stable flight. Instead, it is stabilized actively, by adjustments of the motion of its flapping wings. This is what all flying insects and also hummingbirds do. Flies, for example, sense their body motions with their halteres, drum-stick like biological gyroscopes, and adapt their wing motion accordingly to stay balanced…. or to be agile, when someone is trying to swat them!
And while the Nimble was originally built just to demonstrate that an insect-inspired flying robot can be built, eventually we could also use it to learn more about the flight of insects:
Flapper Drones – how do they work?
The Flapper Nimble+ is the commercial (and enlarged) version of the DelFly Nimble, developed and produced by Flapper Drones. To our knowledge, it is the first, and so far the only hover-capable tailless flapping-wing drone available!
The thrust keeping the Nimble+ airborne is created by its four flapping wings, which flap back and forth horizontally, about 10 to 12 times per second.
The wing actuation mechanism allows to adjust the flapping frequency of the left and right wing pairs independently, which enables control of the roll rotation. Pitch rotation is controlled by adjusting the mean wing position within the stroke plane, which shifts the mean thrust force forward or backward with respect to the center of mass, and also introduces a stabilizing dihedral angle in forward flight. Finally, yawing motion is achieved by tilting the wing roots of the left and right wing pair asymmetrically:
Advantages of flapping wings
The use of flapping-wing drones such as the Flapper Nimble+ brings several advantages. Next to their attractive biological appearance, the soft flapping wings produce less intrusive, low frequency sound and are safer, compared to propellers. As the wings move back and forth, minor mid-air collisions are not a problem. The wings bounce off objects leaving no damage, and the drone keeps flying as this only represents a minor disturbance:
The aerial drag characteristic is also different and helps with precise indoor flight. As soon as zero attitude is commanded, the Nimble+ goes into halt in a matter of several wingbeats, making it an ideal choice for novice drone pilots as well as in constrained or cluttered indoor spaces. Finally, the flapping wings can provide additional lift force as they also glide in forward flight. This can improve the power efficiency by over 20%, compared to hovering.
Otherwise, Flapper Drones can be operated as any other drone, with vertical take offs and landings, quick maneuvers and flight in any direction:
Crazyflie Bolt & compatibility
The Flapper Nimble+ is powered by the Crazyflie Bolt 1.1, where the Bolt’s BMI088 IMU and STM32F4 MCU are suitable substitutes to the halteres and brains of the real fly. We made this choice, because this enables compatibility with most of the Crazyflie ecosystem, but also, because we felt the only way a Crazyflie would do justice to its name is if it had flapping wings😊
Currently, the Nimble+ uses a fork of the Crazyflie firmware, which is of course open source. Moreover, with the recently introduced platform functionality, we will be able to include the Flapper platform into the official crazyflie firmware very soon (expected still in July 2022). This means that the Flapper remains compatible with the official Python libraries, the PC client or the smartphone app. But also third-party projects like the Crazyswarm or the Skybrush should only require minor adjustments, if any, to operate a swarm of Flappers. Thus, for the existing Crazyflie users, switching from a Crazyflie to the Flapper should be a breeze!
The Flapper Nimble+ is hardware compatible with most of the Crazyflie expansion decks. While software support remains experimental (the Flapper Nimble+ is not a native Crazyflie product, after all), many of the decks work out of the box and others might need just minor firmware modifications. Would you like to fly the Nimble+ autonomously? Add an LPS or Lighthouse deck and you’re good to go!
For more details regarding deck compatibility, you can check this overview.
Applications
While the Nimble+ was originally designed for drone shows and similar entertainment applications, the open-source firmware and expansion decks enabling autonomous flight make it ideal also as for academic research and, in general, as a development platform. Are you researching swarming, and would you like to make your swarm even more bio-inspired? Are you developing new sensors, or new controllers (possibly even bioinspired), which you would like to test on a new type of flying platform? Are you interested in the aerodynamics of flapping wings, or the flight dynamics of insect-like flight? Or are you just curious and would you like to learn more about bioinspired flight? In all these cases, a Flapper might be what you are looking for!
The 114-g and 49-cm wide Flapper Nimble+ has been designed as a modular system where any part can easily be replaced. Flapper Drones provides all the spares, which are available upon request. If you are interested in using the Nimble+ for entertainment, rather than research, you can modify the appearance by creating your own body shells, which can also be illuminated by RGB Leds (a suitable interface and power supply is already integrated). Or even by altering the design of the wings. Finally, you can easily extend the Flapper with your own sensors, or other devices. Would you like to add a tail? A gripper? A perching device? This is all possible, as long as these additions fit within the payload limit of about 25 grams.
Available soon in the webstore!
Did you get (bio)inspired, and would you like to try an insect-like flying robot yourself? Then we have some good news! The Flapper Nimble+ will soon be available for sale in an exclusive partnership with Bitcraze and their webstore. Checkout the product description and leave your email address behind, such that you get a notification when the Flappers are in stock and ready to ship. The first batch of 10 units is expected to be available at the end of summer, so do not wait too long 😉
Want to learn more?
To learn more about Flapper Drones, you can check our website, or watch the talk I gave at the last miniBAM:
In December we had a blogpost where we gave an overview of existing simulation models that were out there. In the mean time, I have done some work during my Fun Fridays to get this to work even further. Currently I moved the efforts from my personal Github repo to the Bitcraze organization github called crazyflie-simulation. It is all still very much work in progress but in this blogpost I will explain the content of the repository and what these elements can already do.
Low Poly CAD model
The first thing that you will need to have for any simulation, is a 3D model of the Crazyflie. There is of course already great models available from the CrazyS project, the sim_cf project and the multi_uav_simulator, which are completely fine to use as well. But since we have direct access to the exact geometries of the real crazyflie itself, I wanted to see if I could abstract the shapes myself. And also I would like to improve my Blender skills, so this seemed to be a nice project to work with! Moreover, it might be handy to have a central place if anybody is looking for a 3D simulation model of the Crazyflie.
For simulations with only one or a few Crazyflie, the higher resolution models from the other repository are absolutely sufficient, especially if you are not using a very complicated physics geometry model (because that is where most of the computation is). But if you would like to simulate very big swarms, then the polygon count will have more influences on the speed of the simulation. So I managed to make it to 1970 vertices with the below Crazyflie model, which is not too bad! I am sure that we can make it even with lesser polygons but this is perhaps a good place to start out with for now.
In the crazyflie-simulation, you can find the Blender, stl files and collada files under the folder ‘meshes’.
Webots model
We implemented the above model in a Webots simulator, which was much easier to implement than I thought! The tutorials they provide are great so I was able to get the model flying within a day or two. By combining the propeller node and rotational motor, and adjusting the thrust and drag coefficient to be a bit more ‘Crazyflie like’, it was able to take off. It would be nice to perhaps base these coefficients on the system identification of the Crazyflie, like what was done for this bachelor thesis, but for now our goal is just to make it fly!
It would then be possible to control the pitch and roll with the arrow keys of your keyboard while it is maintaining a current height of 1 meter. This is current state of the code as of commit 79640a.
Ignition Gazebo model
Ignition will be the replacement for Gazebo Classic, which is already a well known simulator for many of you. Writing controllers and plugins is slightly more challenging as it is only in C++ but it is such a landmark in the world of simulation, it only makes sense that we will try to make a Gazebo model of the Crazyflie as well! In the previous blogpost I mentioned that I already experimented a bit with Ignition Gazebo, as it has the nice multicopter motor model plugin standard within the framework now. Then I tried to make it controllable with the intergrated multicopter velocity control plugin but I wasn’t super successful, probably because I didn’t have the right coefficients and gains! I will rekindle these efforts another time, but if anybody would like to try that out, please do so!
First I made my own controller plugin for the gazebo model, which can be found in the repository in a different branch under /gazebo-ignition/. This controller plugin needs to be built first and it’s bin file added to the path IGN_GAZEBO_SYSTEM_PLUGIN_PATH, and the Crazyflie model in IGN_GAZEBO_RESOURCE_PATH , but then if you try to fly the model with the following:
It will take off and hover nicely. Unfortunately, if you try out the key publisher widget with the arrow keys, you see that the Crazyflie immediately crashes. So there is still something fishy there! Please check out the issue list of the repo to check the state on that.
Controllers
So the reason why I made my own controller plugins for the above mentioned simulation models, is that I want to experiment with a way that we can separate the crazyflie firmware controllers, make a code wrapper for them, and use those controllers directly in the simulator. So this way it will become a hybrid software in the loop without having to compile the entire firmware that contains all kinds of extra things that the simulation probably does not need. We can’t do this hybrid SITL yet, but at least it would be nice to have the elements in place to make it possible.
Currently I’m only experimenting with a simple fixed height and attitude PID controller written in C, and some extra files to make it possible to make a python wrapper for those. The C-controller itself you can try out in Webots as of this commit 79640a, but hopefully we will have the python version of it working too.
What is next?
As you probably noticed, the simulation work are still very much work in process and there is still a lot enhancements to add or fix. Currently this is only done on available Fridays so the progress is not super fast unfortunately, but at least there is one model flying.
Some other elements that we would like to work on:
Velocity controller, so that the models are able to react on twist messages.
Crazyflie firmware bindings of controllers
Better system variables (at least so that the ign gazebo model and the webots model are more similar)
I might turn a couple of these into topics that would be good for contribution, so that any community members can help out with. Please keep an eye on the issue list, and we are communicating on the Crazyswarm2 Discussion page about simulations if you want to share your thoughts on this as well.
In this blogpost we will teach you how to fly the Crazyflie beyond edges without crashing, using only on-board sensors. Come join in!
Safe flights across edges are achievable!
Introduction
UAVs have seen tremendous progress in the last decades and have since moved from research labs to various real-world environments. Small UAVs (so-called micro air vehicles, MAVs) like the Crazyflie open up even more possibilities. For example, their size allows them to traverse narrow passages or fly in cluttered environments (as recently showcased in this blog post). However, in order to achieve these complex tasks the community must further improve the cognitive ability of these MAVs in order to avoid crashes.
One task on this list and today’s topic is the possibility to fly at constant altitude irrespective of the terrain. This feature has been discussed in the community already two years ago. To understand the problem, let’s look at the currently implemented solution: With the Flow deck mounted the Crazyflie uses a 1D lidar sensor to estimate its vertical position. This vertical position (more or less) equals the current sensor reading. On flat floors this solution works very well. However, if the Crazyflie shall traverse through a narrow window or fly above irregular terrain its altitude will change based on the sensor readings. This can lead to unstable flights, as in the following video, or even crashes!
You might wonder: why not use any of the other great tools from the Bitcraze universe? Indeed, the Lighthouse positioning system and the Loco positioning system work well for absolute positioning (as we have seen earlier, e.g., in this blog post). However, the required setups are often not available in difficult environments. Alternatively, the barometer could be used to achieve a solution based solely on on-board sensors. In fact, Bitcraze has proposed an altitude hold functionality a few years ago. This is a cool feature, but its positioning accuracy of “roughly ±15cm” is not fully satisfying. Finally, relying on the on-board IMU alone will inevitably lead to drifting over time.
Thus, we propose a solution based on the Flow deck and the Multiranger deck. This approach, only based on on-board sensors, allows to fly at constant altitude with obstacles above, below, or even both above and below the Crazyflie. Kristoffer Skare developed this solution when he worked with us as an intern in 2021.
Technical Description
As a first step, the upward-facing lidar of the Multiranger deck is incorporated in the same way the downward-facing lidar of the Flow deck is used in the firmware. This additional measurement can then be used in the extended Kalman filter (EKF) to improve the state estimation. Currently, the EKF estimates and outputs 1 value for the altitude. For our purpose two more states are added to the EKF: one state is defined as the height of the object under the Crazyflie compared to the height where the altitude state is defined as 0. Similarly, the other state is defined as the height of the object above the Crazyflie compared to the same reference height. The Crazyflie keeps therefore track of the environment in order to keep its own altitude constant. To achieve this, an edge detection was implemented: The errors between the predicted and measured distance are tracked in both the upward or downward range measurement. If either of these errors is too large the algorithm assumes that the floor or roof has changed (while the original EKF would think the drone’s position has changed, triggering a change in thrust). Thus, the corresponding state gets updated. For more details on the technical implementation and the code itself, check out our pull request.
Results
To analyze our approach we have used a Qualisys motion capture system. We have conducted many different tests: flying over different obstacles, flying at different velocities, flying at different altitudes, or even flying under different lighting conditions. Exemplarily, in this post we will have a look at a baseline example, a good estimate, and a bad estimate. In each picture you can see the altitude (in meters) over time (in seconds) for different flight speeds (in centimeters per second). You will see three lines: The motion capture ground truth (blue), the altitude estimated by our code (orange), and the new state keeping track of the floor height (green). For each plot, the Crazyflie takes off, flies in positive x direction, and lands.
In the baseline experiment, it flies over a flat floor. Clearly, the altitude estimates follow the ground truth values well, and the floor is correctly estimated to be flat.
Baseline experiment flying over a flat floor
In the next example, we have added a box with an approximate height of 0.225 m and made the Crazyflie fly over it. Despite the obstacle the altitude estimates follow the ground truth values well. Note how the floor estimates indicates the shape of the box.
Experiment flying over a box
Because the algorithm is based on an edge detection, we had a hunch that smoothly changing obstacles will pose a problem. Indeed, the estimates can be messy as we see in the next example. Here, the Crazyflie flies over an orthogonal triangle, with the short leg at 0.23 m pointing upwards and the long leg with 0.65 m pointing in flight direction (thus forming a slope). For different flight speeds different the estimates turn out quite differently.
Experiment flying over a slope
If you don’t like looking at plots, check out this video with some cool shots instead!
Conclusion
To summarize, we propose a solution for constant altitude flight with Crazyflies, using the Flow deck and the Multiranger deck. We have tested it successfully under various circumstances. Still, we see some potential for improvement, e.g. when dealing with slopes. In addition, the current implementation is quite a change to the original EKF, which poses a problem for integration.
Thus, a way forward can be an out-of-tree build to ease the use of the solution for the community. At SINTEF we certainly plan to deploy this code in all of our tests in 2022, which will hopefully allow us to gather more experience and thus find further ways to improve or tune the system.
We want to emphasize that this is not a perfect solution. That means a) you should use it with care and b) you are very much welcomed to contribute. E.g. feel free to chime in in the pull request, test the code in your environments, propose improvements, or implement an out-of-tree build! :) Maybe you can even come up with an alternative approach for constant altitude flights?
If you want to check out more of our work, visit our website. Also, keep reading this amazing blog from Bitcraze as we try to be back some day (if Bitcraze wants us hehe)!
