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]
Trust (calibrated using 200g weight)
Torque, calibrated weight [g]
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!
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 . 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 . Depending on the characteristics of their reflected echos, bats can even classify certain objects — this ability helps them find, for instance, water sources . Wouldn’t it be amazing to harvest these findings in building novel navigation systems for autonomous agents such as drones or cars?
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
In our paper on the system , 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).
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 , 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.
 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.
 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.
 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.
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.
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).
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.
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!
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).
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.
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  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  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 . 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  and M-SAM  were developed in 2021 and 2022 respectively.
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).
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.
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.
 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.
 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.
 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.
 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.
 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.
 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.
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 , 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 , 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.
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 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 , we introduced two types of modules which are R-modules and T-modules.
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.
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.
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.
 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.
 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.
 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.
 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.
 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.
Keeping things in stock has not been easy the last couple of years due to the general problems with availability of components. We have been mitigating this by increasing stock volumes when it has been possible, but we have also looked at redesigns of some products to be able to switch to other components. A positive side effect has been that it also enabled us to do some small changes we wanted to do for a long time.
The decks we have updated are the Lighthouse, SD-card and BigQuad decks. There are no big functionality changes so the decks have not gotten any updated version only a new board revision.
Lighthouse (Rev.D -> Rev.D1) The outline of the PCB has changed a bit in the hope of protecting the photo-diode sensors a bit better during hard crashes.
SD-card (Rev.C -> Rev.D) Some solder bridges were added to the bottom of the PCB to make it easier to utilize the “hidden” SPI port. This can be useful if wanting to log a lot of values to the SD-card in combination with decks using the SPI port as well, such as the Loco or Flow decks. See the datasheet for more details.
Biq-Quad (Rev.C -> Rev.C1) The capacitor C1 was removed. This was used to filter the analog current measurement reading but also caused problem for the SPI bus on the deck port. The SPI bus turned out to be a more used functionality and therefore capacitor C1 was removed. If the analog filtering functionality is wanted, a 100nF 0603 capacitor can be soldered to C1.
From now on we ship the updated revisions if you order in our store.
Previously we have been using off the shelf scales and other methods to measure characteristics, such as thrust or efficiency, of the Crazyflie products. We thought it was time to build something that is easier to use, more repeatable and tailored to our needs. Well, this has been on our wanted list for a long time, already back from when we did the RPM-deck. It was however first when Wolfgang visited us this winter that he nudged us over the edge so we finally allocated some time for it. We started off by buying some load cells and breakout boards to do something simple as a start, so we could at least measure thrust more easily. We actually started looking for off the shelf thrust stands but could not find anything suitable for the Crazyflie’s size. As is often the case here at Bitcraze, the project grew. Already before we had any load cells up and running I was designing a deck with RPM sensors, load cell amplifier and power meter. Now with the objective to easily do system identification. Therefor we named the deck the system-id deck.
For the RPM sensors we used the same as on the RPM-deck, the QRD1114. They are not great as they need a reflective surface, this means adding white stickers or paint to black propellers, but they work well enough. The load cell amplifiers ended up to be the NAU7802 as it has a high accuracy and sample rate. For power metering we chose the new ACS37800 power monitoring IC that can handle up to 30A, this looked exiting.
The QRD1114 we wired the same way as previously done on the RPM-deck:
The NAU7802 was configured as per the datasheet suggestion and similarly to other open designs out there:
The ACS37800 was very new so the datasheet had to be used as the main information source. A bit tricky as this chip is mainly intended to measure mains supply, and we wanted to measure low voltage DC, which it said it could do…and in the end we managed to get it working.
We also added a buck/boost DC/DC that could provide a stable 3.3V from 2-5V input, just in case, as the ACS37800 is specified for this voltage and not the 3.0V the Crazyflie can supply.
The PCB was designed as small as possible so it could be mounted on a Crazyflie 2.X and used while flying. A bonus would be if it could be used on a Bolt as well.
Here it is mounted on a Crazyflie 2.1 together with a 3D printed stand and load cell.
The load cell would then be mounted to a desk or similar so the the Crazyflie is mounted up-side down, pushing down on the load cell.
The software, as often, took most of the time to make. Three major deck driver files was created, rpm.c, acs37800.c and loadcell_nau7802.c. Aside from these there where only small changes to make, like making it work when being up-side-down. The modifications have all been pushed to the dev-systemid branch for those that are interested. As for now we are mainly using the logging framework to transfer the data to the PC, which is quick and easy to setup and use, but writing to SD-card is also possible. The scripts for this can be found in the tools/sytem_id folder.
Remaining work is to test, gather and analyse more data. When we have done so, we will post more. Until then below is a sample of what it can measure. The data is taken with a ramping PWM from 0% – 100% – 0%. The added resistance of the extra wires and connectors are not taken into account, but the estimated efficiency of 4g/W is probably not that far off.
This week we have a guest blog post from Bart Duisterhof and Prof. Guido de Croon from the MAVlab, Faculty of Aerospace Engineering from the Delft University of Technology. Enjoy!
Tiny drones are ideal candidates for fully autonomous jobs that are too dangerous or time-consuming for humans. A commonly shared dream would be to have swarms of such drones help in search-and-rescue scenarios, for instance to localize gas leaks without endangering human lives. Drones like the CrazyFlie are ideal for such tasks, since they are small enough to navigate in narrow spaces, safe, agile, and very inexpensive. However, their small footprint also makes the design of an autonomous swarm extremely challenging, both from a software and hardware perspective.
From a software perspective, it is really challenging to come up with an algorithm capable of autonomous and collaborative navigation within such tight resource constraints. State-of-the-art solutions like SLAM require too much memory and processing power. A promising line of work is to use bug algorithms , which can be implemented as computationally efficient finite state machines (FSMs), and can navigate around obstacles without requiring a map.
A downside of using FSMs is that the resulting behavior can be very sensitive to their hyperparameters, and therefore may not generalize outside of the tested environments. This is especially true for the problem of gas source localization (GSL), as wind conditions and obstacle configurations drastically change the problem. In this blog post, we show how we tackled the complex problem of swarm GSL in cluttered environments by using a simple bug algorithm with evolved parameters, and then tested it onboard a fully autonomous swarm of CrazyFlies. We will focus on the problems that were encountered along the way, and the design choices we made as a result. At the end of this post, we will also add a short discussion about the future of nano drones.
Why gas source localization?
Overall we are interested in finding novel ways to enable autonomy on constrained devices, like CrazyFlies. Two years ago, we showed that a swarm of CrazyFlie drones was able to explore unknown, cluttered environments and come back to the base station. Since then, we have been working on an even more complex task: using such a swarm for Gas Source Localization (GSL).
There has been a lot of research focussing on autonomous GSL in robotics, since it is an important but very hard problem. The difficulty of the task comes from the complexity of how odor can spread in an environment. In an empty room without wind, a gas will slowly diffuse from the source. This can allow a robot to find it by moving up gradient, just like small bacteria like E. Coli do. However, if the environment becomes larger with many obstacles and walls, and wind comes into play, the spreading of gas is much less regular. Large parts of the environment may have no gas or wind at all, while at the same time there may be pockets of gas away from the source. Moreover, chemical sensors for robots are much less capable than the smelling organs of animals. Available chemical sensors for robots are typically less sensitive, noisier, and much slower.
Due to these difficulties, most work in the GSL field has focused on a single robot that has to find a gas source in environments that are relatively small and without obstacles. Relatively recently, there have been studies in which groups of robots solve this task in a collaborative fashion, for example with Particle Swarm Optimization (PSO). This allows robots to find the source and escape local maxima when present. Until now this concept has been shown in simulation  and on large outdoor drones equipped with LiDAR and GPS , but never before on tiny drones in complex, GPS-denied, indoor environments.
In our project, we introduce a new bug algorithm, Sniffy Bug, which uses PSO for gas source localization. In order to tune the FSM of Sniffy Bug, we used an artificial evolution. For time reasons, evolution typically takes place in simulation. However, early in the project, we realized that this would be a challenge, as no end-to-end gas modeling pipeline existed yet. It is important to have an easy-to-use pipeline that does not require any aerodynamics domain knowledge, such that as many researchers as possible can generate environments to test their algorithms. It would also make it easier to compare contributions and to better understand in which conditions certain algorithms work or don’t work. The GADEN ROS package  is a great open-source tool for modeling gas distribution when you have an environment and flow field, but for our objective, we needed a fully automated tool that could generate a great variety of random environments on-demand with just a few parameters. Below is an overview of our simulation pipeline: AutoGDM.
First, we use a procedural environment generator proposed in  to generate random walls and obstacles inside of the environment. An important next step is to generate a 3D flowfield by means of computational fluid dynamics (CFD). A hard requirement for us was that AutoGDM needed to be free to use, so we chose to use the open-source CFD tool OpenFOAM. It’s used for cutting-edge aerodynamics research, and also the tool suggested by the authors of GADEN. Usually, using OpenFOAM isn’t trivial, as a large number of parameters need to be selected that require field expertise, resulting in a complicated process. Next, we integrate GADEN into our pipeline, to go from environment definition (CAD files) and a flow field to a gas concentration field. Other parts that needed to be automated were the random selection of boundary conditions, which has a large impact on the actual flow field, and source placement, which has an equally large impact on the concentration field.
After we built this pipeline, we started looking for a robot simulator to couple it to. Since we weren’t planning on using a camera, our main requirement was for the simulator to be efficient (preferably in 2D) so that evolutions would take relatively little time. We decided to use Swarmulator , a lightweight C++ robot simulator designed for swarming and we plugged in our gas data.
Roughly speaking, we considered two categories of algorithms for controlling the drones: 1) a neural network, and 2) an FSM that included PSO, with evolved parameters. Since we used a tiny neural network for light seeking with a CrazyFlie in our previous work, we first evolved neural networks in simulation. One of the first experiments is shown below.
While it worked pretty well in simple environments with few obstacles, it seemed challenging to make this work in real life with complex obstacles and multiple agents that need to collaborate. Given the time constraints of the project, we have opted for evolving the FSM. This also facilitated crossing the reality gap, as the simulated evolution could build on basic behaviors that we developed and validated on the real platform, including obstacle avoidance with four tiny laser rangers, while communicating with and avoiding other drones. An additional advantage of PSO with respect to the reality gap is that it only needs gas concentration and no gradient of the gas concentration or wind direction (which many algorithms in literature use). On a real robot at this scale, estimating the gas concentration gradient or the direction of a light breeze is hard if not impossible.
Our CrazyFlie needs to be able to avoid obstacles, execute velocity commands, sense gas, and estimate the other agent’s position in its own frame. For navigation, we added the flow deck and laser rangers, whereas for gas sensing we used a TGS8100 gas sensor that was used on a CrazyFlie before in previous work . The sensor is lightweight and inexpensive, but accurately estimating gas concentrations can be difficult because of its size. It tends to drift and needs time to recover after a spike in concentration is observed. Another thing we noticed is that it is possible to break them, a crash can definitely destroy the sensor.
To estimate the relative position between agents, we use a Decawave Ultra-Wideband (UWB) module and communicate states, as proposed in . We also use the UWB module to communicate gas information between agents and collaboratively seek the source. The complete configuration is visible below.
Evaluation in Simulation
After we optimized the parameters of our model using Swarmulator and AutoGDM, and of course trying many different versions of our algorithm, we ended up with the final Sniffy Bug algorithm. Below is a video that shows evolved Sniffy Bug evaluated in six different environments. The red dots are an agent’s personal target waypoint, whereas the yellow dot is the best-known position for the swarm.
Simulation showed that Sniffy Bug is effective at locating the gas source in randomly generated environments. The drones successfully collaborate by means of PSO.
Real Flight Testing
After observing Sniffy Bug in simulation we were optimistic, but unsure about performance in real life. First, inspired by previous works, we disperse alcohol through the air by placing liquid alcohol into a can which is then dispersed using a computer fan.
We test Sniffy Bug in our flight arena of size 10 x 10 meters with large obstacles that are shaped like walls and orange poles. The image below shows four flight tests of Sniffy Bug in cluttered environments, flying fully autonomously, i.e., without the help from any external infrastructure.
In the total of 24 runs we executed, we compared Sniffy Bug with manually selected and evolved parameters. The figure below shows that the evolved parameters are more efficient in locating the source as compared to the manual parameters.
This does not only show that our system can successfully locate a gas source in challenging environments, but it also demonstrates the usefulness of the simulation pipeline. The parameters that were learned in simulation yield a high-performance model, validating the environment generation, randomization, and gas modeling parts of our pipeline.
Conclusion and Discussion
With this work, we believe we have made an important step towards swarms of gas-seeking drones. The proposed solution is shown to work in real flight tests with obstacles, and without any external systems to help in localization or communication. We believe this methodology can be extended to larger environments or even to 3 dimensions, since PSO is a robust, multi-dimensional heuristic search method. Moreover, we hope that AutoGDM will help the community to better compare gas seeking algorithms, and to more easily learn parameters or models in simulation, and deploy them in the real world.
To improve Sniffy Bug’s performance, adding more laser rangers will definitely help. When working with only four laser rangers you realize how little information it actually provides. If one of the rangers senses a low value it is unclear if a slim pole or a massive wall is detected, adding inefficiency to the algorithm. Adding more laser rangers or using other sensor modalities like vision will help to avoid also more complex obstacles than walls and poles in a reliable manner.
Another interesting discussion can be held on the hardware required for real deployment. When working with 40 grams of maximum take-off weight, the sensors and actuators that can be selected are limited. For example, the low-power and lightweight flow deck works great but fails in low-light scenarios or with smoke. Future work exploring novel sensors for highly constrained nano robots could really help increase the Technological Readiness Level (TRL) of these systems.
Finally, this has been a really fun project to work on for us and we can’t wait to hear your thoughts on Sniffy Bug!
Li-Ion batteries have packed more energy per gram for a long time compared to Li-Po batteries. The problem for UAV applications has been that Li-Ion can’t deliver enough current, something that is starting to change. Now there are cells that are supposed to be able to deliver 30-35A continuously in the 18650 series, at least according to the specs. Therefor we thought it was time to do some testing and decided to build a 1 cell Li-Ion drone using the Crazyflie Bolt as base.
Since a 18650 battery is 18mm in diameter and 65mm long, the size would affect the design but we still wanted to keep the drone small and lightweight. The battery is below 20mm wide which means we can run the deck connectors around it, that is nice. We chose to use our 3D printer to build the frame and use off the shelf ESCs, motors and props. After a couple of hours of research we selected 3″ propellers, 1202.5 11500kv motors and tiny 1-2s single ESCs for our first prototype.
1 x Custom designed 130mm 3D printed frame
1 x Crazyflie Bolt flight controller
4 x Eachine 3020 propeller (2xCW + 2xCCW)
4 x Flywoo ROBO RB 1202.5 11500 Kv motors
4 x Flash hobby 7A 1-2S ESC
1 x Li-Ion Sony 18650 VTC6 3000mAh 30A
Screws, anti vib. spacers, zipties, etc.
The custom designed frame was developed in iterations, and can still be much improved, but at this stage it is small, lightweight and rigid enough. We wanted the battery to be as central as possible while keeping it all compact.
Assembly and tuning
The 3D printed frame came out quite well and weighed in at 13g. After soldering the bolt connectors to the ESCs, attaching motors and props, adjusting battery cable and soldering a XT30 to the Li-Ion battery it all weighed ~103g and then the battery is 45g of these. It feels quite heavy compared to the Crazyflie 2.1 and we had a lot of respect when we test flew it the first time. Before we took off we reduced the pitch and roll PID gains to roughly half and luckily it flew without problems and quite nicely. Well it sounds a lot but that is kind of expected. After increasing the gains a bit we felt quite pleased with:
This would be good enough for what we really wanted to try, the endurance with a Li-Ion battery. A quick measurement of the current consumption at hover, 5.8A, we estimated up to ~30 min flight time on a 3000mAh Li-Ion battery, wow, but first a real test…
For the hover test we used lighthouse 2 which is starting to work quite well. We had to change the weight and thrust constants in estimator_kalman.c for the autonomous flight to work:
#define CRAZYFLIE_WEIGHT_grams (100.0f)
//thrust is thrust mapped for 65536 &lt;==> 250 GRAMS!
#define CONTROL_TO_ACC (GRAVITY_MAGNITUDE*250.0f/CRAZYFLIE_WEIGHT_grams/65536.0f)
After doing that and creating a hover script that hovers at 0.5m height and was set to land when the voltage reached 3.0V. We leaned back with excitement, behind a safety net, and started the script… after 19 min it landed… good but not what we hoped for and quite far from the calculated 30 min. Maybe Li-Ion isn’t that good when it needs to provide more current…? A quick internet search and we could find that Li-Ion can run all the way down to 2.5V, but we have to stop at 3.0V because of electronics and loosing thrust, so we are missing quite a bit of energy… Further investigations are needed.
Lighthouse 2 flight test
As a final test we launched some flight scripts to fly in a square and in a spiral so we would get a feel for Lighthouse 2 + Bolt with PID controller combination. We think it turned out quite nicely, and this with almost no optimization effort:
Li-Ion felt like it could be a game changer when it comes to flight time but was not as promising as we hoped for. It doesn’t mean we can’t get there though. More research and development is required.
Modular robotics implies in general flexibility and versatility to robots. In theory, you could design a modular robot basically on the way you would want it to be, by simply adding or removing modules from the already existing robot. Changing the robot configuration by adding more individuals, generally increases the system redundancy, meaning that now, there are probably many different ways to achieve a specific goal. From a naive standard point of view, more modules could imply in practice more robustness due to this redundancy. In fact, it does get more robust by the cost of becoming more complex, and probably harder to control. Added to that, other issues may arise when you take into account that your modular robot is flying, and how physical properties and actuation scales as the number of modules grow.
In the GRASP Laboratory at the University of Pennsylvania, one of our focuses is to allow robots to achieve a specific task. In this work, we present ModQuad-DoF, which is a modular flying platform that enlarges the configuration space of modular flying structures based on quadrotors (crazyflies), by applying a new yaw actuation method that relies on the desired roll angles of each flying vehicle. This research project is coordinated by Professor Mark Yim , and led by Bruno Gabrich (PhD candidate).
Scaling Modular Robots
Scaling modular robots is a very challenging problem that usually limits the benefits of modularity. The sum of the performance metrics (speed, torque, precision etc.) from each module usually does not scale at the same rate as the conglomerate physical properties. In particular, for ModQuad, saturation from individual motors would increase as the structures became larger leading to failure and instability. When conglomerate systems scale up in the number of modules, the moment of inertia of the conglomerate often grows faster than the increase in thrust capability for each module. For example, the increase in the moment of inertia for a fifth module added to four modules in a line can be approximated by the mass of the module times half the distance to the center squared. This quadratic increase gives us the intuition that the required yaw actuation grows faster than the actuation authority.
An inherit characteristic of quadrotors is to have their yaw controlled by the drag moments from each propeller. For ModQuad as more modules are docked together, a decreased controllability in yaw is noticed as the structure becomes larger. In a line configuration the structure’s inertia grows quadratically with the distance of each module to the structure center of mass. On the other hand the drag moments produced scales linearly with the number of modules.
The new yaw actuation method relies on the fact that each quadrotor is capable to generate an individual roll enabled by our new cage design. By working in coordinated manner, each crazyflie can then generate structure moments from moment arms provided by the propellers given its roll and its distance from the structure’s center of mass.
The Crazyflie 2.0 is the chosen platform to enable thrust and attitude to the individual modules. The flying vehicle measures 92×92×29 mm and weights 27 g while its battery lasts around 4 minutes for the novel design proposed. In this work the cage performs as pendulum relative to the flying vehicle. The quadrotor is joined to the cage through a one DOF joint. The cages are made of light-weight materials: ABS for the 3-D printed connectors and joints, and carbon fiber for the rods.
Although the flying vehicle does not necessarily share same orientation as the cage, the multiple connected cages do preserve same orientation relative to each other. With the purpose of allowing such behavior, we used Neodymium Iron Boron (NdFeB) magnets as passive actuators to enable rigid cage connections. Docking is only allowed at the back and front face of the modules, and each one of these faces contains four magnets. Those passive actuators have dimensions of 6.35 × 6.35 × 0.79 mm with a bonding force of 1 kg.
Structure Flying Performance
ModQuad-DoF is a flying modular robotic structure whose yaw actuation scales with increased numbers of modules. ModQuad-DoF has a one DOF jointed cage design and a novel control method for the flying structure. Our new yaw actuation method was validated conducting experiments for hovering conditions. We were able to perform two, four and, six modules cooperatively flying in a line with yaw controllability and reduced loss in thrust. In future work we aim to explore the structure controllability with more robots in a line configuration, and exploring different solutions for the desired roll angles. Possibly, with more modules in the structure, only a few would be required to roll in order to maintain a desired structure yaw. Given that, we could explore the control allocation for each module in a specific structure configuration, and dependent on its desired behavior. Further, structures that are not constrained to a line will also be tested using the basis of the controller proposed in this work.