Crazyflie Brushless charging pad

2024-09-23 | Marcus | 1 Comment

Ever since we started going to fairs to show off the Crazyflies, we’ve been trying to push the boundaries for the demos. Often we’ve used the fairs as an opportunity to either develop new functionality or try out new ideas. Something we’ve always been interested in, especially for fairs, is autonomous flights. It’s hard to talk to people about the Crazyflie while trying to fly it at the same time. Back in 2015 we were using the Kinect for piloting the Crazyflie at the Bay Area Maker Faire. Although awesome, we had a slight issue: we needed to switch batteries on the Crazyflie each flight. We had a Qi deck for wireless charging but no positioning system good enough to use it for landing on a charger.

Latest iteration of the Crazyflie Brushless charger

In 2018 we were really excited when we got to borrow a motion capture system from Qualisys and could finally land on a Qi charger (3D printed base and an IKEA Qi charger). First time we showed this off was at IROS in Madrid 2018. The following year we improved the demo to have more Crazyflies and switched to the Lighthouse positioning system at ICRA 2019. Since then each year we have been improving the demo until we’ve reached the current state we showed off at IROS 2022 in Kyoto.

So since 2018 we’ve been using the Qi wireless charging for our demos. Many customers have purchased the Qi charging deck, but building a matching charging platform has always required some effort. So, a while back we started looking at something that could replace the Qi deck, with a lighter solution which would also allow users to have other decks with electronics facing downwards. The first prototypes were made with the Crazyflie 2.1 back in 2021 using decks, but they were a bit clumsy. For one thing you needed the charging solution to be integrated on each deck.

When work started on the Crazyflie Brushless we realized we had the possibility to integrate the charge points directly on the main PCB which meant we could still use any decks we wanted and get the charging. So the prototypes from 2021 were reshaped into something we could use with the Crazyflie Brushless. Although the prototypes worked well, they were pretty big and packed with features which weren’t needed for charging (like LED lights and WiFi). Another iteration and the chargers have now gone down in size and complexity. The latest iteration only has charging and is powered via our 12V power block or 5V USB-C.

Over the years lots of customers have asked us for buying the Qi charger, since many users do not have the capabilities to build their own. Unfortunately we’ve never gotten around to it, but with the release of the Crazyflie Brushless we would like to change this. The release is only a few months away so we’re short on time for remaking the design so it’s usable for plastic molding. Instead the plan is to make a limited amount of prototypes available to our users, based on the same 3D printed design and electronics we’re currently using in our flight lab, at the time of release. This will enable our users to easily try out the design and create their own autonomous demos which will keep flying for a long time.

The optimized Crazyflie 2.1 brushless motors

2024-08-12 | Tobias | Leave a comment

For the upcoming Crazyflie 2.1 brushless we developed, together with a leading motor manufacturing brand, a brushless 08028 motor, targeting high quality and high efficiency. The 08 – stator size motors are usually optimized for high power output, to serve the FPV market, but we where aiming for high efficiency. This means fitting maximum amount of copper around the stator, lowering KV, thin stator lamination sheets and high quality dual ball-bearings.

Specification

Stator size: 08028 (8.4mm x 2.8mm)
Stator lamination sheets: 0.2mm
Motor KV: 10000
Internal resistance: 0.52 Ohm
Weight: 2.4g
Dual ball-bearing design, using high quality NSK or NMB brands.
1 mm shaft, 5 mm length
Matching propeller: Bitcraze 55-35mm
Peak current 1.8A, peak power 7.2W -> 30g thurst @ 4V (using 55-35)
Rated voltage: 4.2V

Together with the bitcraze 55-35 mm propeller we manage to achieve a system efficiency of over 5 W/g during hover, not to shabby. As a reference, FPV setups normally achieve around 2 W/g. This will bring the hover time for the Crazyflie 2.1 brushless, in the barebone configuration, a bit over 10 minutes.

Discover the Impact of Structural Defects on Nano-Quadcopter Performance: CrazyPAD dataset

2024-06-24 | Tagir Muslimov | Leave a comment

This week we have a guest blogpost by Kamil Masalimov (MSc) and Tagir Muslimov (PhD) of the Ufa University of Science and Technology. Enjoy!

As researchers passionate about UAV technology, we are excited to share our recent findings on how structural defects affect the performance of nano-quadcopters. Our study, titled “CrazyPAD: A Dataset for Assessing the Impact of Structural Defects on Nano-Quadcopter Performance,” offers comprehensive insights that could greatly benefit the Crazyflie community and the broader UAV industry.

The Motivation Behind Our Research

Understanding the nuances of how structural defects impact UAV performance is crucial for advancing the design, testing, and maintenance of these devices. Even minor imperfections can lead to significant changes in flight behavior, affecting stability, power consumption, and control responsiveness. Our goal was to create a robust dataset (CrazyPAD) that documents these effects and can be used for further research and development.

Key Findings from Our Study

We conducted a series of experiments by introducing various defects, such as added weights and propeller cuts (Figure 1), to nano-quadcopters. For the experiments, we used the Lighthouse Positioning System with two SteamVR 2.0 virtual reality stations (Figure 2).

**Figure 1. Propeller with two side defects**

**Figure 2. Schematic of the experimental setup with Lighthouse Positioning System**

Here are some of the pivotal findings from our research:

Stability Impact: We observed that both added weights and propeller cuts lead to noticeable changes in the stability of the quadcopter. Larger defects caused greater instability, emphasizing the importance of precise manufacturing and regular maintenance.
Increased Power Consumption: Our experiments showed that structural defects result in higher power consumption. This insight is vital for optimizing battery life and enhancing energy efficiency during flights.
Variable Control Responsiveness: We used the standard deviation of thrust commands as a measure of control responsiveness. The results indicated that defects increased the variability of control inputs, which could affect maneuverability and flight precision.
Changes in Roll and Pitch Rates: The study also highlighted variations in roll and pitch rates due to structural defects, providing a deeper understanding of how these imperfections impact flight dynamics.

We show Figure 3 as an example of a graph obtained from our dataset. In this figure, you can see the altitude and thrust command over time for different flight conditions. The blue line represents the normal flight, while the orange line represents the flight with additional weight near the M3 propeller. In Figure 4, you can see the 3D flight trajectory of the Crazyflie 2.1 quadcopter under the cut_propeller_M3_2mm condition with the corrected ideal path. The blue line represents the actual flight trajectory, while the red dashed line with markers represents the ideal trajectory. Figure 5 shows the Motor PWM values over time for the add_weight_W1_near_M3 condition. The plot shows the PWM values of each motor (M1, M2, M3, and M4) as they respond to the added weight near the M3 propeller.

More examples of graphs obtained from the CrazyPAD dataset can be found in our research paper specifically describing this dataset: https://doi.org/10.3390/data9060079

**Figure 3. Altitude and thrust command over time for different flight conditions**

**Figure 4. 3D flight trajectory of the Crazyflie 2.1**

**Figure 5. Motor PWM values over time**

Leveraging Research for Diagnostic and Predictive Models

One of the most exciting aspects of our research is its potential application in developing diagnostic and predictive models. The CrazyPAD dataset can be utilized to train machine learning algorithms that detect and predict structural defects in real-time. By analyzing flight data, these models can identify early signs of wear and tear, allowing for proactive maintenance and reducing the risk of in-flight failures.

Diagnostic models can continuously monitor the performance of a UAV, identifying anomalies and pinpointing potential defects. This real-time monitoring can significantly enhance the reliability and safety of UAV operations.

Predictive models can forecast future defects based on historical flight data. By anticipating when and where defects are likely to occur, these models can inform maintenance schedules, ensuring UAVs are serviced before issues become critical.

Why This Matters for the Crazyflie Community

The CrazyPAD dataset and our findings offer valuable resources for the Crazyflie community. By understanding how different defects affect flight performance, developers and enthusiasts can improve design protocols, enhance testing procedures, and ensure higher safety and performance standards for their UAVs.

We believe that sharing our research with the Crazyflie community can lead to significant advancements in UAV technology. The dataset we created is open under the MIT License for further exploration and can serve as a foundation for new innovations and improvements.

Get Involved and Explore Further

We invite community members to explore our full research article and the CrazyPAD dataset. Together, we can drive forward the standards of UAV technology, ensuring that Crazyflie remains at the forefront of innovation and excellence.

Our research paper with a detailed description of this dataset:

Masalimov, K.; Muslimov, T.; Kozlov, E.; Munasypov, R. CrazyPAD: A Dataset for Assessing the Impact of Structural Defects on Nano-Quadcopter Performance. Data 2024, 9, 79. ht tps://doi.org/10.3390/data9060079

Dataset: https://github.com/AerialRoboticsUUST/CrazyPAD

We are eager to collaborate with the Crazyflie community and welcome any feedback or questions regarding our research. Let’s work together to push the boundaries of what’s possible in UAV technology.

The Crazyflie 2.1 brushless is getting optional propeller guards

2024-05-21 | Tobias | 3 Comments

One of the great features of the stock Crazyflie 2.1 is that it is more or less harmless. The Crazyflie 2.1 brushless weighs roughly the same but has almost twice the amount of thrust force, so a little bit of more care is needed. We therefore decided to provide optional propeller guards. While propeller guards adds safety they also add weight and disrupt the air flow from the propellers. Adding to that, the weight is located far from the center which increases the inertia even further, resulting in a less agile drone. For some applications this is not a problem but for others it is, this is why we are making them optional, meaning they are easy to replace with simple landing legs by utilizing a snap-on fitting.

The design is not fully finalized yet but we are getting close, voilá!

If the design goes according to plan they will also withstand some bumping against walls which will be a very nice feature for many applications.

Further the landing legs and propeller guards are designed in a way so they will detach during high force impacts to prevent the PCB arms from breaking.

New Crazyflie propeller 47-17

2023-09-05 | Tobias | Leave a comment

Last week our brand new 47-17 (47mm diameter, 17mm pitch) Crazyflie 2.X propeller became available in black and green in the shop! It is a custom designed propeller for the 0.8mm shaft, 7×16 coreless brushed motor, that comes with the Crazyflie 2.X. The improved design boosts the efficiency, both flight time and maximum thrust is increased with up to 15%. It is made in polycarbonate (PC) which makes it more durable so that it will withstand crashes better. The new propeller is better then the stock 45-17 in almost all areas except in noise where the new 47-17 propeller runs at a higher RPM. Below is a graph comparing the two propellers using the thrust stand we previously built. The graph is a bit messy but hopefully you can figure it out! The big takeaway is that the 45-35 propeller tops at ~4 g/W while the 47-17 tops at ~4.7 g/W using the stock 7×16 motor.

Green: PWM output, 1 = 100%, Bottom Red/Blue: thrust, Jagged Red/Blue: Efficiency [g/W],
Staircase Red/Blue: kRPM.

The Crazyflie 2.1 kit will continue to be shipped with the “stock” 45-35 propeller. At some point we want to switch to the new propeller in the kit. We don’t know when this will happen yet and will of course announce it here at that point :-).

New 350mA Li-Po on the way

2023-06-19 | Tobias | Leave a comment

We are happy to announce that we are working on a new upgrade battery for the Crazyflies! It will soon hit production and hopefully, keeping our fingers crossed, it will arrive in our stock in early 2023-Q4.

The upgrade battery is based on the “Tattu 350mAh 3.7V 30C 1S1P” cell and with some additional great features:

Protection Circuit Module (PCM) to protect against short circuits, overcharge, over discharge etc.
Gold-plated connectors for lower contact resistance.
Shrink wrap around connector for better rigidity.
Cool Bitcraze matched graphics.

And if we list the benefits compared to the stock Crazyflie battery:

Higher current capabilities, 30C burst current, that is >10 Amp.
350mAh instead of 250mAh
Higher energy density, ~130 Wh/kg instead of ~105 Wh/kg

There are some drawbacks too:

It is ~1 mm thicker and does not fit well with all deck boards and the short or medium size pin headers. We will release longer pin headers at the same time though.
Price will be higher
~1.5 grams extra weight

With this upgrade battery, you will experience longer flight times, more “punch” during acceleration and it is great combined with the thrust upgrade kit!

Modifying a thrust stand for small motors

2023-05-08 | Tobias | 2 Comments

When designing flying robots like drones it is important to be able to benchmark and test the propulsion system which in this case is a speed controller, motor and propeller. As we at Bitcraze are mainly working with tiny drones we need a thrust stand designed for small motors and propellers. We have actually already designed our own system identification deck, which can measure overall efficiency, thrust, etc., but is lacking the ability to measure torque. Torque is needed to be able to measure propeller efficiency which is now something we would like to measure. Before we developed the system-id deck we searched for of the shelf solutions that could satisfy our needs and could not find any. This still seems true, please let us know if that isn’t the case.

Expanding the system-id deck to measure torque doesn’t work and building something from scratch was a too big of a project for us. Next natural option would then be to modify an existing thrust stand and our choice fell for the tyro robotics 158X series.

Looking at specifications, images and code we could figure out that replacing the load cells for more sensitive ones should be possible. The stock setup of 5kgf thrust and 2Nm of torque is just too much as we are looking for around 100 grams of thrust and around 10 mNm of torque. So we decided to give the replacement of load cells a shot! Assembly was quite smooth but we managed to break one of the surface mount load cell connectors off, luckily this was easily fixable with a soldering iron. With the stock setup we did some measurements with a 0802 11000KV brushless motor and a 55mm propeller in a pushing setup. It works but the measurements are noisy and repeatability is not great. Next thing would be to replace the load cells. The 158X uses TAL221 sized load cells which are available down to 1kg. We got those and with a calibration-allways-pass code we got from Tyto robotics we could make the calibration pass (note that modifying the thrust stand breaks the warranty). Now the thrust stability was much better but still the torque was a bit to noisy. We decided to go for even smaller thrust cells, the TAL220, and build 3D printable adapters to make them fit.

Now the torque noise level looked much better and so did the repeatability. By empirically measuring the thrust and torque using calibrated weights and by checking the measurements in RCBenchmark we got these values:

Thrust, calibrated weight [g]	Measured [g]	Noise [g]
200	200	1
100	100	1
50	50	0.5
20	20	0.5
10	10	0.5
0	0	0.5

Trust (calibrated using 200g weight)

Torque, calibrated weight [g]	Measured [mNm]	Noise [mNm]
200	257	2
100	128	1
50	64	0.3
20	25.7	0.3
10	12.7	0.3
0	0	0.2

Torque (calibrated using 200g weight)

The thrust stand modification is still very fresh and we have to figure out some things but it all looks promising. For example we get 13% less overall efficiency when measuring it using our system-id thrust stand. Our guess is that it is due to that the Crazyflie arms in the system-id case blocks the airflow.

If you would like to do this modification yourself there are some simple instructions and STL files over at out mechanical github repository. Have fun!

From Crazyflies to “Crazybats”

2023-01-16 | Frederike Dümbgen | 2 Comments

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.

¹The 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.

The Big Loco Test Show

2022-12-26 | Barbara | 4 Comments

This year, the traditional Christmas video was overtaken by a big project that we had at the end of November: creating a test show with the help of CollMot.

First, a little context: CollMot is a show company based in Hungary that we’ve partnered with on a regular basis, having brainstorms about show drones and discussing possibilities for indoor drones shows in general. They developed Skybrush, an open- source software for controlling swarms. We have wanted to work with them for a long time.

So, when the opportunity came to rent an old train hall that we visit often (because it’s right next to our office and hosts good street food), we jumped on it. The place itself is huge, with massive pillars, pits for train maintenance, high ceiling with metal beams and a really funky industrial look. The idea was to do a technology test and try out if we could scale up the Loco positioning system to a larger space. This was also the perfect time to invite the guys at CollMot for some exploring and hacking.

The Loco system

We added the TDoA3 Long Range mode recently and we had done experiments in our test-lab that indicate that the Loco Positioning systems should work in a bigger space with up to 20 anchors, but we had not actually tested it in a larger space.

The maximum radio range between anchors is probably up to around 40 meters in the Long Range mode, but we decided to set up a system that was only around 25×25 meters, with 9 anchors in the ceiling and 9 anchors on the floor placed in 3 by 3 matrices. The reason we did not go bigger is that the height of the space is around 7-8 meters and we did not want to end up with a system that is too wide in relation to the height, this would reduce Z accuracy. This setup gave us 4 cells of 12x12x7 meters which should be OK.

Finding a solution to get the anchors up to the 8 meters ceiling – and getting them down easily was also a headscratcher, but with some ingenuity (and meat hooks!) we managed to create a system. We only had the hall for 2 days before filming at night, and setting up the anchors on the ceiling took a big chunk out of the first day.

Drone hardware

We used 20 Crazyflie 2.1 equipped with the Loco deck, LED-rings, thrust upgrade kit and tattu 350 mAh batteries. We soldered the pin-headers to the Loco decks for better rigidity but also because it adds a bit more “height-adjust-ability” for the 350 mAh battery which is a bit thicker then the stock battery. To make the LED-ring more visible from the sides we created a diffuser that we 3D-printed in white PLA. The full assembly weighed in at 41 grams. With the LED-ring lit up almost all of the time we concluded that the show-flight should not be longer than 3-4 minutes (with some flight time margin).

The show

CollMot, on their end, designed the whole show using Skyscript and Skybrush Studio. The aim was to have relatively simple and easily changeable formations to be able to test a lot of different things, like the large area, speed, or synchronicity. They joined us on the second day to implement the choreography, and share their knowledge about drone shows.

We got some time afterwards to discuss a lot of things, and enjoy some nice beers and dinner after a job well done. We even had time on the third day, before dismantling everything, to experiment a lot more in this huge space and got some interesting data.

What did we learn?

Initially we had problems with positioning, we got outliers and lost tracking sometimes. Finally we managed to trace the problems to the outlier filter. The filter was written a long time ago and the current implementation was optimized for 8 anchors in a smaller space, which did not really work in this setup. After some tweaking the problem was solved, but we need to improve the filter for generic support of different system setups in the future.

Another problem that was observed is that the Z-estimate tends to get an offset that “sticks” and it is not corrected over time. We do not really understand this and will require more investigations.

The outlier filer was the only major problem that we had to solve, otherwise the Loco system mainly performed as expected and we are very happy with the result! The changes in the firmware is available in this, slightly hackish branch.

We also spent some time testing maximum velocities. For the horizontal velocities the Crazyflies started loosing positioning over 3 m/s. They could probably go much faster but the outlier filter started having problems at higher speeds. Also the overshoot became larger the faster we flew which most likely could be solved with better controller tuning. For the vertical velocity 3 m/s was also the maximum, limited by the deceleration when coming downwards. Some improvements can be made here.

Conclusion is that many things works really well but there are still some optimizations and improvements that could be made to make it even more robust and accurate.

The video

But, enough talking, here is the never-seen-before New Year’s Eve video

And if you’re curious to see behind the scenes

Thanks to CollMot for their presence and valuable expertise, and InDiscourse for arranging the video!

And with the final blogpost of 2022 and this amazing video, it’s time to wish you a nice New Year’s Eve and a happy beginning of 2023!

Single Actuator Monocopters (SAMs)

2022-08-08 | xinyu | Leave a comment

This week’s guest blogpost is from Xinyu Cai from the research group of ShaoHui Foong, located in the Engineering Product Development Faculty from Singapore University of Technology and Design. Please check out their youtube channel. Enjoy!

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.