Category: Video

“What? You are in Japan? Again!?”. Yup that is right! We loved IROS Kyoto 2022 so much that we just couldn’t wait to come back again. Barbara, Arnaud and Rik are setting up the booth as we speak to show some Bitcraze awesomeness to you! Come and say hi at booth IC085.

The gang before the rush starts!

Crazyflie Brushless and Camera expansion

Of all the prototypes we are the most excited of showing you the Crazyflie Brushless and the ‘forward facing expansion connector prototype’ aka the Camera deck. Here you can see them both in action at a tryout of our demo. We have also written blogposts about both so make sure to read them as well (Brushless blogpost, Camera expansion blogpost)

The Crazyflie Brushless flying with a Camera deck.

Also we will explain about the contact charging prototype (see the blogpost here) and will be showing all of our decks at the booth as well. And of course our fully autonomous, onboard, decentralized peer-to-peer and avoiding swarm demo will be displayed as always. Make sure to read this blogpost of when we showed this demo at IROS 2022 to understand what is fully going on!

Also take a look at our event page of the ICRA 2024 demo.

Hand in your Crazyflie posters at our booth!

We will be providing a ‘special disposal service’ for your conference poster! We would love to see what you are working on and get your poster, because we have a lot of space in our updated office/flight space but a lot of empty walls.

If you hand in your poster at the booth, you’ll get a special, one-of-a-kind, button badge that you can wear proudly during the conference! So we will see you at booth IC085!

The ‘Bitcraze took my poster’ button!

It’s not often a blog post happens on the 25th of December, so this time, you’re having a treat with some new Bitcraze prototypes as a present from us! If you have time to get away from the Christmas table, there’s something we’d love you to watch:

Now let’s try to see if you noticed all the new stuff you see in this video!

Our new flight lab

We teased it, but in the beginning of December, we got our extended flight lab! We added 110 m2 to our flight space. It was a rush to have everything ready for the video – we cleaned everything, painted the walls and the green logo, set up the positioning system without our truss… But now we’re happy to show you how big the space is! Even if it’s hard to convey the real size on camera.

The Crazyflie 2.1 brushless

We already talked about it in this blog post, but the brushless has made significant progress and we feel confident that you will get your hands on it in 2024. Here, we use the extra power for a fast and agile flight. It also was very stable and didn’t crash once during the shooting!

The Lighthouse V2

Yes, you counted right! The Brushless flew with 16 base stations! We’ve worked really hard this past three months to create a new Lighthouse deck – the Lighthouse deck 2.0. It could get its position from 16 base stations. That’s 4 times more than what was previously possible! It behaved consistently well during the different tries, and we are really happy with the result. Right now, it’s just a prototype, but we’re hoping to get it to the next step in the coming months.

The contact charging station

Marcus created a power charger for the Brushless that doesn’t need any extra deck to allow for charging. It connects with the brushless feet. It has also the cool feature of changing LEDs indicating the status (idle, charging or charged). It is also a prototype, and we don’t know if this will end up being a product

The high-power LED

This is trickier to see, but it’s not our usual LED ring that the brushless carries. It’s a new, powerful LED underneath. It is so powerful that it nearly blinded us when we tried it for the first time. We put a diffuser on it, and it allowed the Crazyflie to be visible at such a high pace! This is a prototype too of course and we’re not sure if we will release it, but it’s fun to use for this kind of project.

Other announcements

During this week, our office is closed- we take this week to celebrate and rest a little before 2024. This means that shipping and support will be greatly reduced.

But we’re back the week after- at a somewhat reduced pace though. The developer meeting on the 3rd of January is maintained but without any presentation. We’ll take this time to answer any questions you have and talk a little! The details are here.

Bitcraze got their presents this year: a handful of working prototypes! We hope we got your wishes too, merry Christmas to you!

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

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

Theory of the Flow deck

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

Image from Positioning System Overview blogpost

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

Image from state estimation repo documentation

Tips & Tricks and Limitations

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

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

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

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

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

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

Developer meeting about Flow deck

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

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

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

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

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

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

We first tested with some symmetric triangle and quad formations.

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

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

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

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

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

The corresponding publication can be found here: https://link.springer.com/article/10.1007/s10846-023-01842-1

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

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

YouTube Link: https://www.youtube.com/watch?v=nA41uJIehH8&t=1s

Today, Vivek Adajania from Learning Systems and Robotics lab write about a project for a safe motion planning of Crazyflie swarm that was published at ICRA 2023. Enjoy!

Motivation

Quadrotor swarms offer significant potential in applications like search and rescue, environmental mapping, and payload transport due to their flexibility and robustness compared to single quadrotors. The core challenge in these applications is collision-free and kinematically feasible trajectory planning. As the quadrotors share space, they must safely manoeuvre around each other and avoid collisions with static obstacles. Existing solutions [1] [2], while effective for generating collision-free trajectories, often struggle in densely cluttered scenarios due to simplifying approximations.

Background

There are two literature groups in the domain of optimization-based quadrotor swarm motion planning: centralized and distributed approaches. In a centralized setup, a central computer solves a joint optimization problem that computes trajectories for all quadrotors at once. These approaches have broad solution space but quickly become computationally intractable as the number of quadrotors increases. On the other hand, the distributed approach involves each quadrotor independently solving its optimization problem and incorporating trajectories shared by the neighbouring quadrotors. This strategy offers improved scalability, yet existing distributed approaches struggle in cluttered environments.

Fig. Centralized and distributed planning approach to quadrotor swarm motion planning. The arrows indicate the flow of communication.

In this work, we adopt a distributed planning strategy. The independent optimization problem that needs to be solved by each of the quadrotors in the distributed setup is a non-convex quadratically constrained quadratic program (QCQP). This nature of the problem stems from non-convex and quadratic collision avoidance constraints and kinematic constraints.

Existing distributed approaches rely on sequential convex programming (SCP) that performs conservative approximations to obtain a quadratic program (QP). First, linearization of the collision avoidance constraints to obtain affine hyperplane constraints. Second, axis-wise decoupling of the kinematic constraints to obtain affine box constraints. We obtain a QP but with small feasible sets.

Fig. Conservative approximations made by Sequential Convex Programming (SCP) based approaches.

Proposed Approach

In contrast, our proposed approach obtains a QP without relying on the previously mentioned approximations. The first ingredient is the polar reformulation of collision avoidance and kinematic constraints. An example of the 2D polar reformulation of collision avoidance constraints is shown below:

Fig. Example illustration of polar reformulation of 2D collision avoidance constraints.

The second ingredient is to relax the reformulated constraints as l-2 penalties into the cost function and apply Alternating Minimization. Alternating Minimization results in subproblems that are convex QPs, and some have closed-form solutions, thus obtaining a QP form without relying on linearization; further details can be found in our paper [3]. We can also use and reformulate alternative collision avoidance constraints, barrier function (BF) constraints

where hij is the Euclidean distance between quadrotor i and quadrotor j, and the parameter γ controls how fast the quadrotor i is allowed to approach the boundary of quadrotor j.  

Results

We experimentally demonstrate our approach on a 12 Crazyflie 2.0 swarm testbed in challenging scenes: obstacle-free, obstacle-rich, shared workspace with a human. The experimental video is provided below:

In the simulation, we compare our approach against two SCP approaches: SCP (Continuous) [2] enforces constraints across the entire horizon, while SCP (On-demand) [1] enforces only on the first predicted collision. Our (Axiswise) includes box kinematic constraints, while Our (Quadratic) preserves the original quadratic constraints.

From our simulation results, we see that SCP (On-demand) has a lower compute time than SCP (Continuous), as SCP (On-demand) enforces fewer constraints. But, this compute time trend comes at the expense of success rate. On the contrary, our approaches achieve a high success rate with low compute times. Ours (Quadratic) has a slightly higher success rate than Ours (Axiswise) as it has access to large kinematic bounds.

Fig. Simulation results from 100 start-goal configurations with swarm sizes ranging from 10 to 50 in a cluttered environment with 16 cylindrical static obstacles.

Fig. Simulation results from 100 start-goal configurations with swarm sizes ranging from 10 to 50 and three different γvalues in a cluttered environment with 16 cylindrical static obstacles.

On average, our approaches achieved a 72% success rate improvement, a 36% reduction in mission time, and 42x faster per-agent computation time—our approach trades-off mission time with inter-agent clearance and distance to obstacles via BF constraints.

Outlook

In this work, we presented an online and scalable trajectory planning algorithm for quadrotor swarms in cluttered environments that do not rely on the linearization of collision avoidance constraints and axis-wise decoupling of kinematic constraints. We do so by reformulating the quadratic constraints to a  polar form and applying alternating minimization to the resulting problem. Consequently, our planner achieves high scalability and low computation times than existing approaches. We also show that we can reformulate barrier function constraints to introduce safety behaviours in the swarm. One of the future works is to extend the approach to navigate the swarm in a complex 3D environment.

References

[1] Luis, Carlos E., Marijan Vukosavljev, and Angela P. Schoellig. “Online trajectory generation with distributed model predictive control for multi-robot motion planning.” IEEE Robotics and Automation Letters 5.2 (2020): 604-611.

[2] E. Soria, F. Schiano and D. Floreano, “Distributed Predictive Drone Swarms in Cluttered Environments,” in IEEE Robotics and Automation Letters, vol. 7, no. 1, pp. 73-80, Jan. 2022, doi: 10.1109/LRA.2021.3118091.

[3] V. K. Adajania, S. Zhou, A. K. Singh and A. P. Schoellig, “AMSwarm: An Alternating Minimization Approach for Safe Motion Planning of Quadrotor Swarms in Cluttered Environments,” 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 1421-1427, doi: 10.1109/ICRA48891.2023.10161063.

Links

The authors are with the Learning Systems and Robotics Lab at the University of Toronto and the Technical University of Munich. The authors are also affiliated with the Vector Institute for Artificial Intelligence and the University of Toronto Robotics Institute (RI) in Canada and the Munich Institute of Robotics and Machine Intelligence (MIRMI) in Germany.

Feel free to contact us with any questions or ideas: vivek.adajania@robotics.utias.utoronto.ca. Please cite this as:

@INPROCEEDINGS{
adajania2023amswarm, 
author={Adajania, Vivek K. and Zhou, Siqi and Singh, Arun Kumar and Schoellig, Angela P.}, 
booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)}, 
title={AMSwarm: An Alternating Minimization Approach for Safe Motion Planning of Quadrotor Swarms in Cluttered Environments}, 
year={2023}, 
pages={1421-1427}, 
doi={10.1109/ICRA48891.2023.10161063} 
}

As of this year around March/April we started with both Bitcraze developer meetings and Aerial-ROS meetings (the latter in collaboration with Dronecode Foundation). Now that summer is around and our office is a bit empty, we had a bit of a summer break, however we will start the meetings back up again soon! The next ROS-aerial meeting will be on the 16th of August and we will also have a Bitcraze developer meeting planned on the Wednesday the 6th of September (keep an eye on our announcements in discussions). In this blogpost we like to take the opportunity to show an overview of the meetings we had so far.

Aerial ROS meetings

In March we started a [ROS community working group] for aerial Vehicles together with our friends at Dronecode foundation, aka Aerial-ROS! We have biweekly meetings with some standard discussion meetings (with a topic) and with an invited guest presentation.

Here are the discussion topic meetings we had:

And we had several guest speakers as well! Like Miguel Fernandez-Cortizas from CAR-UPM talking about Aerostack2:

ROS-aerial Meeting guest presentation about Aerostack2

Then we had a guest presentation from Gerald Peklar from NXP talking about the Drones4Bats project:

ROS-aerial Meeting guest presentation about Drones4Bats

And the last before the summer was from Alejandro Hernández Cordero (Open Robotics Consultant) about the ROS2 Project Vehicle Gateway.

ROS-aerial Meeting guest presentation about Vehicle Gateway

The next meeting for ROS-aerial is planned on the 16th of August. Keep an eye on the ROS discourse forum.

Bitcraze Developer Meetings

We already had a couple of developer meetings before but we started recording them since April. The first recorded one was about the loco positioning system. Here first we gave a presentation about the system itself, with the latest developments cooking in our pot and time for questions afterwards.

Dev meeting about Loco positioning.

Then we had a meeting about the development of safety features in the Crazyflie in light of the Bolt developments:

Bitcraze Dev meeting about Safety features.

Then we had a meeting where Kristoffer highlighted the autonomous swarm demo we showed at ICRA 2023.

Bitcraze dev meeting about the autonomous swarm demo

And the last before the summer holiday, we had a meeting where Kimberly explained about the Crazyflie simulationmodel intergrated into Webots

Bitcraze Dev meeting about Simulation

We are still planning to have developer meeting every first wednesday of the month starting with September 6th (keep an eye on our announcements in discussions).

EPFL 101 Crazyflie presentation

Oh yeah, by the way, we also were invited by the EPFL-lis lab to give another Crazyflie 101 presentation in Lausanne last April! We made a prerecording of it so you can check it out right here:

EPFL LIS crazyflie 101 presentation.

See you all after summer!

This week’s guest blogpost is from Matěj Karásek from Flapper Drones, about flying the Nimble + with a positioning system. Enjoy!

Flapper Drones are bioinspired robots flying by flapping their wings, similar to insects and hummingbirds. If you haven’t heard of Flappers yet, you can read more about their origins at TU Delft and about how they function in an earlier post and on our company website.

In this blogpost, I will write about how to fly the Flappers (namely the Flapper Nimble+) autonomously within a positioning system such as the Lighthouse, and will of course include some nice videos as well.

The Flapper Nimble+ is the first hover-capable flapping-wing drone on the market. It is a development platform powered by the Crazyflie Bolt and so it can enjoy most of the perks of the Crazyflie ecosystem, including the positioning systems as well as other sensors (check this overview). If you would like to get a Flapper yourself, just head to the Bitcraze webstore, where there are some units ready to be shipped! (At the time of writing at least…)

Minimal setup

The minimal setup for flying in a positioning system is nearly identical as with a standard Crazyflie. Next to a Flapper with a recent firmware, a Crazyradio dongle, a positioning system (in this post we will use the Lighthouse), and a compatible positioning deck (Lighthouse deck) you will also need: 1) a mount, such that the deck can be attached on top of the Flapper, and 2) a set of extension cables. You can 3D print the mounts yourself (models here), the extension cable prototypes can either be inquired from Flapper Drones, or can be soldered by yourself (in that case, the battery holder deck, standard Crazyflie pin headers and some wires come handy). Just pay attention to connect the cables in the correct way, as if the deck was mounted right on top of the Bolt. The complete setup with the Lighthouse deck will look like this:

Lighthouse deck installation on a Flapper Nimble+. Make sure the extension cables are well secured (e.g. by using the additional cable mount) such they don’t get caught by the gears.

For the Lighthouse, as with regular Crazyflies, the minimum number of base stations (with some redundancy) is 2, but you will get larger tracking volume with more base stations. 4 base stations mounted at 3 m height will give you about 5 meters time 5 meters coverage, which is recommended especially if you want to fly more than 1 Flapper at a time (they are a bit larger than the Crazyflies, after all…).
From now on, it is exactly the same as with standard Crazyflies. After you calibrate the Lighthouse system using the standard wizard procedure via the Cfclient, you can just go to the Flight Control Tab and use the “Command Based Flight Control” buttons to take-off, command steps in xyz directions and land. It is this easy!

Flapper Nimble+ in Lighthouse flown via Command Based Flight Control of cfclient

Assisted flight demo

We used this setup in February for the demos we were giving at the Highlight Delft festival in the Netherlands. This allowed people with no drone piloting skills (from 3-year-olds, to grandmas – true story) fly and control the Flapper in a safe way (safe for the Flapper, as the Flapper itself is a very safe platform thanks to its soft wings and low weight). To make it more fun, and even safer for the Flapper, we used a gamepad instead of on screen buttons, and we modified the cfclient slightly such that the flight space can be geofenced to stay within the tracking volume.

Flight demo at Highlight Delft festival, using the Lighthouse and position hold assistance

If you would like to try it yourself (it works also with standard crazyflies), the source code is here (just keep in mind it is experimental and has some known bugs…). To fly in the position-assisted mode, you need to press (and keep pressing) the Alt 1 button, and use the joysticks to move around (velocity commands, headless mode). Releasing the Alt 1 button will make the Flapper autoland. Autoland will also get triggered when the battery is low. You can still fly the Flapper in a direct way when pressing Alt 2 instead.

Flying more Flappers at a time

Again, this is something that works pretty much out of the box. As with a regular crazyflie, you just need to assign a unique address to each of the Flappers and then use e.g. this example python script to run a preprogrammed sequence.

With a few extra lines of code, we pulled this quick demo at the end of the Highlight Delft festival, when we had 30 minutes left before packing everything (one of the Flappers decided to drop its landing gear, probably too tired after 3 evenings of almost continuous flying…):

Sequence with 3 Flappers within Lighthouse positioning system

Other positioning systems

Using other positioning systems is equally easy. In fact, for the Loco Positioning system, the deck can even be installed directly on the Flapper’s Bolt board (no extension cables or mounts are needed). As for optical motion tracking, we do not have experience with Qualisys and the active marker deck, but flying with retro-reflective markers within OptiTrack system can be setup easily with just a few hacks.

When choosing and setting up the positioning system, just keep in mind that due to its wings, the Flapper needs to tilt much more to fly forward or sideways, compared to a quadcopter. This is not an issue with the Loco Positioning system (but there can be challenges with position estimation, as described further), but it can be a limitation for systems requiring direct line of sight, such as the Lighthouse or optical motion tracking.

Ongoing work

In terms of control and flight dynamics, the Flapper is very different from the Crazyflie. Thus, for autonomous flight, there remains room for improvement on the firmware side. We managed to include the “flapper” platform into the standard Crazyflie firmware (in master branch since November 2022, and in all releases since then), such that RC flying and other basic functionality works out of the box. However, as many things in the firmware were originally written only for a (specific) quadcopter platform, the Crazyflie 2.x, further contributions are needed to unlock the full potential of the Flapper.

With the introduction of “platforms” last year, many things can be defined per platform (e.g. the PID controller gains, sensor alignment, filter settings, etc.), but e.g. the Extended Kalman filter, and specifically the motion model inside, has been derived and tuned for the Crazyflie 2.x, and is thus no representative of the Flapper with very different flight dynamics. This is what directly affects (and currently limits) the autonomous flight within positioning systems – it works well enough at hover and slow flight, but the agility and speed achievable in RC flight cannot be reached yet. We are planning to improve this in the future (hopefully with the help of the community). The recently introduced out of tree controllers and estimators might be the way to go… To be continued :)

Thanks Matej ! And for those of you at home, don’t forget that we have our dev meeting next Wednesday (the 5th), where we’ll discuss about the Loco positioning system, but also will take some time for general discussions. We hope to see you there!

It’s time for a new compilation video about how the Crazyflie is used in research ! The last one featured already a lot of awesome work, but a lot happened since then, both in research and at Bitcraze.

As usual, the hardest about making those videos is choosing the works we want to feature – if every cool video of the Crazyflie was in there, it would last for hours! So it’s just a selection of the most videogenic projects we’ve seen. You can find a more extensive list of our products used in research here.

We’ve seen a lot of projects that used the modularity of the Crazyflie to create awesome new features, like a catenary robot, some wall tracking or having it land upside down. The Crazyflie board was even made into a revolving wing drone. New sensors were used, to sniff out gas leaks (the Sniffy bug as seen in this blogpost), or to allow autonomous navigation. Swarms are also a research topic where we see a lot of the Crazyflie, this time for collision avoidance, or path planning. We also see more and more of simulators, which are used for huge swarms or physics tests.

Once again, we were surprised and awed by all the awesome things that the community did with the Crazyflie. Hopefully, this will inspire others to think of new things to do as well. We hope that we can continue with helping you to make your ideas fly, and don’t hesitate to share with us the awesome projects you’re working on!

Here is a list of all the research that has been included in the video:

And, without further ado, here it is:

My name is Hanna, and I just started as an intern at Bitcraze. However, it is not my first time working with a drone or even the Crazyflie, so I’ll tell you a bit about how I ended up here.

The first time I used a drone, actually even a Crazyflie, was in a semester thesis at ETH Zurich in 2017, where my task was to extend a Crazyflie with a Parallel Ultra Low-Power (PULP) System-on-Chip (SoC) connected to a camera and external memory. This was the first prototype of the AI-deck you can buy here nowadays (as used here) :)

My next drone adventure was an internship at a company building tethered drones for firefighters – a much bigger system than the Crazyflie. I was in charge of the update system, so more on the firmware side this time. It was a very interesting experience, but I swore never to build a system with more than three microcontrollers in it again.

This and a liking for tiny and restricted embedded systems brought me back to the smaller drones again. I did my master thesis back at ETH about developing a PULP-based nano-drone (nano-drones are just tiny drones that fit approximately in the palm of your hand and use only around 10Watts of power, the category Crazyflies fit in) and some onboard intelligence for it. As a starting point, we used the Crazyflie, both for the hardware and the software. It turned out to be a very hard task to port the firmware to a processor with only a very basic operating system at that time. Still, eventually I knew almost every last detail of the Crazyflie firmware, and it actually flew.

However, for this to happen, I needed some more time than the master thesis – in the meantime, I started to pursue a PhD at ETH Zurich. I am working towards autonomous miniaturized drones – so besides the part with the tiny PULP-based drone I already told you about, I also work on the “autonomous” part. Contrary to many other labs our focus is not only on novel algorithms though, we also work with novel sensors and processors. Two very interesting recent developments for us are a multi-zone Time-of-Flight sensor and the novel gap9 processor, which both fit on a Crazyflie in terms of power, size and weight. This enables new possibilities in obstacle avoidance, localization, mapping and many more. Last year my colleagues and I already posted a blog post about our newest advances in obstacle avoidance (here, with Videos!). More recently, we worked on onboard localization, using novel multi-zone Time-of-Flight sensors and the very new GAP9 processor to execute Monte Carlo localization onboard a Crazyflie (arxiv).

On the left you see an example of a multi-zone Time-of-Flight image (the background is a picture from the AI-deck), from here. On the right you see our prototype for localization in action – from our DATE23 paper (arxiv).

For me, localizing with a given map is a fascinating topic and one of the reasons I ended up in Sweden. It is one of the most basic skills of robots or even humans to navigate from A to B as fast as possible, and the basis of my favourite sport. The sport is called “orienteering” and is about running as fast as possible to some checkpoints on a map, usually through a forest. It is a very common sport in Sweden, which is the reason I started learning Swedish some years ago. So when the opportunity to go to Malmö for some months to join Bitcraze presented itself, I was happy to take it – not only because I like the company philosophy, but also because I just like to run around in Swedish forests :)

Now I am looking forward to my time here, I hope to learn lots about drones, firmware, new sensors, production, testing, company organization and to meet a lot of new nice people!

Greetings from Malmö – it can be a bit cold and rainy, but the sea and landscape are beautiful!

Hanna

This week’s guest blogpost is from Frederike Dümbgen presenting her latest work from her PhD project at the Laboratory of Audiovisual Communications (LCAV), EPFL, and is currently a Postdoc at the University of Toronto. Enjoy!

Bats navigate using sound. As a matter of fact, the ears of a bat are so much better developed than their eyes that bats cope better with being blindfolded than they cope with their ears being covered. It was precisely this experiment that helped the discovery of echolocation, which is the principle bats use to navigate [1]. Broadly speaking, in echolocation, bats emit ultrasonic chirps and listen for their echos to perceive their surroundings. Since its discovery in the 18th century, astonishing facts about this navigation system have been revealed — for instance, bats vary chirps depending on the task at hand: a chirp that’s good for locating prey might not be good for detecting obstacles and vice versa [2]. Depending on the characteristics of their reflected echos, bats can even classify certain objects — this ability helps them find, for instance, water sources [3]. Wouldn’t it be amazing to harvest these findings in building novel navigation systems for autonomous agents such as drones or cars?

Figure 1: Meet “Crazybat”: the Crazyflie equipped with our custom audio deck including 4 microphones, a buzzer, and a microcontroller. Together, they can be used for bat-like echolocation. The design files and firmware of the audio extension deck are openly available, as is a ROS2-based software stack for audio-based navigation. We hope that fellow researchers can use this as a starting point for further pushing the limits of audio-based navigation in robotics. More details can be found in [4].

The quest for the answer to this question led us — a group of researchers from the École Polytechnique Fédérale de Lausanne (EPFL) — to design the first audio extension deck for the Crazyflie drone, effectively turning it into a “Crazybat” (Figure 1). The Crazybat has four microphones, a simple piezo buzzer, and an additional microprocessor used to extract relevant information from audio data, to be sent to the main processor. All of these additional capabilities are provided by the audio extension deck, for which both the firmware and hardware design files are openly available.1

Video 1: Proof of concept of distance/angle estimation in a semi-static setup. The drone is moved using a stepper motor. More details can be found in [4].

In our paper on the system [4], we show how to use chirps to detect nearby obstacles such as glass walls. Difficult to detect using a laser or cameras, glass walls are excellent sound reflectors and thus a good candidate for audio-based navigation. We show in a first semi-static feasibility study that we can locate the glass wall with centimeter accuracy, even in the presence of loud propeller noise (Video 1). When moving to a flying drone and different kinds of reflectors, the problem becomes significantly more challenging: motion jitter, varying propeller noise and tight real-time constraints make the problem much harder to solve. Nevertheless, first experiments suggest that sound-based wall detection and avoidance is possible (Figure and Video 2).

Video 2: The “Crazybat” drone actively avoiding obstacles based on sound.
Figure 2: Qualitative results of sound-based wall localization on the flying “Crazybat” drone. More details can be found in [4].

The principle we use to make this work is sound-based interference. The sound will “bounce off” the wall, and the reflected and direct sound will interfere either constructively or destructively, depending on the frequency and distance to the wall. Using this same principle for the four microphones, both the angle and the distance of the closest wall can be estimated. This is however not the only way to navigate using sound; in fact, our software stack, available as an open-source package for ROS2, also allows the Crazybat to extract the phase differences of incoming sound at the four microphones, which can be used to determine the location of an external sound source. We believe that a truly intelligent Crazybat would be able to switch between different operating modes depending on the conditions, just like bats that change their chirps depending on the task at hand.

Note that the ROS2 software stack is not limited to the Crazybat only — we have isolated the hardware-dependent components so that the audio-based navigation algorithms can be ported to any platform. As an example, we include results on the small wheeled e-puck2 robot in [4], which shows better performance than the Crazybat thanks to the absence of propeller noise and motion jitter.

This research project has taught us many things, above all an even greater admiration for the abilities of bats! Dealing with sound is pretty hard and very different from other prevalent sensing modalities such as cameras or lasers. Nevertheless, we believe it is an interesting alternative for scenarios with poor eyesight, limited computing power or memory. We hope that other researchers will join us in the quest of exploiting audio for navigation, and we hope that the tools that we make publicly available — both the hardware and software stack — lower the entry barrier for new researchers. 

1 The audio extension deck works in a “plug-and-play” fashion like all other extension decks of the Crazyflie. It has been tested in combination with the flow deck, for stable flight in the absence of a more advanced localization system. The deck performs frequency analysis on incoming raw audio data from the 4 microphones, and sends the relevant information over to the Crazyflie drone where it is converted to the CRTP protocol on a custom driver and sent to the base station for further processing in the ROS2 stack.

References

[1] Galambos, Robert. “The Avoidance of Obstacles by Flying Bats: Spallanzani’s Ideas (1794) and Later Theories.” Isis 34, no. 2 (1942): 132–40. https://doi.org/10.1086/347764.

[2] Fenton, M. Brock, Alan D. Grinnell, Arthur N. Popper, and Richard R. Fay, eds. “Bat Bioacoustics.” In Springer Handbook of Auditory Research, 1992. https://doi.org/10.1007/978-1-4939-3527-7.

[3] Greif, Stefan, and Björn M Siemers. “Innate Recognition of Water Bodies in Echolocating Bats.” Nature Communications 1, no. 106 (2010): 1–6. https://doi.org/10.1038/ncomms1110.

[4] F. Dümbgen, A. Hoffet, M. Kolundžija, A. Scholefield and M. Vetterli, “Blind as a Bat: Audible Echolocation on Small Robots,” in IEEE Robotics and Automation Letters (Early Access), 2022. https://doi.org/10.1109/LRA.2022.3194669.