Ever since we developed the new 47-17 propellers it’s been on our list to update the Crazyflie 2.1 kit. And finally it is here! While we were at it, we also updated the battery which is now 1 gram lighter with the same performance and capacity. These changes will improve the flight duration and thrust with up to 15%.
An assembled Crazyflie 2.1+
At the same time, we will discontinue the Crazyflie 2.1 as the Crazyflie 2.1+ replaces the 2.1. If you still need the old propeller, don’t worry, we will continue selling this.
Increasing prices
As with the rest of the world, we’re feeling the impact of inflation, and like many others, we’re having to make some adjustments to keep up with the rising costs. We’ve done our best to keep things steady, but in order for us to keep developing our products we’ve realized that a small price adjustment across our product line is necessary. So starting today, August 19th, you’ll notice a slight increase of up to 10% on our products.
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.
A few weeks ago, the prestigious Robotics: Science and Systems (RSS) conference was held at Delft University of Technology. We helped with the co-organization of a half-day tutorial and workshop called “Aerial Swarm Tools and Applications” so Kimberly (I) was there on behalf of both Bitcraze and Crazyswarm2. In this blog post, we will tell you a bit about the conference itself and the workshop (and perhaps also a tiny bit about RoboCup)
The Robotics: Science and Systems conference
The Robotics: Science and Systems conference, also known as RSS, is considered one of the most important robotics conferences to attend, alongside ICRA and IROS. It distinguishes itself by having only a single track of presented papers, which makes it possible for all attendees to listen to and learn about all the cool robotics work done in a wide range of fields. It also makes it more difficult to get a paper accepted due to the fixed number of papers they can accept, so you know that whatever gets presented is of high quality.
This year the topic was very much on large language models (LLMs) and their application in robotics, most commonly manipulators. Many researchers are exploring the ways that LLMs could be used for robotics, but that means not a lot of small and embedded systems were represented in these papers. We did find one paper where Crazyflies were presented, namely the awesome work by Darrick et al. (2024) called ‘Stein Variational Ergodic Search’ which used optimal control for path planning to achieve the best coverage.
It gave us the chance to experience many of the other works that could be found at RSS. One in particular was about the robotic design of the cute little biped from Disney Imagineering named “Design and Control of a Bipedal Robotic Character” by Grandia et al. (2024). Also very impressive was the Agile flight demo by the group of Davide Scaramuzza, and we enjoyed listening to the keynote by Dieter Fox, senior director at Nvidia, talking about ‘Where is RobotGPT?’. The banquet location was also very special, as it was located right in the old church of Delft.
The main reason we joined RSS was that we were co-organizing the workshop ‘Aerial Swarm Tools and Applications’. This was done in collaboration with Wolfgang Hönig from Crazyswarm2/TU Berlin, Miguel Fernandez Cortizas and Rafel Perez Segui from Aerostack2/Polytechnic University of Madrid (UPM), and Andrea Testa, Lorenzo Pichierri, and Giuseppe Notarstefano from CrazyChoir/University of Bologna. The workshop was a bit of a hybrid as it contained both talks on various aerial swarm applications and tutorials on the different aerial swarm tools that the committee members were representatives of.
Photos of the Aerial Swarm Tools and Applications workshop
Sabine Hauert from the University of Bristol started off the workshop by talking about “Trustworthy swarms for large-scale environmental monitoring.” Gábor Vásárhelyi from Collmot Robotics and Eötvös University gave a talk/tutorial about Skybrush, showing its suitability not only for drone shows but also for research (Skybrush was used for the Big Loco Test show demo we did 1.5 years ago). The third speaker was SiQi Zhou, speaking on behalf of Angela Schöllig from TU Munich, discussing “Safe Decision-Making for Aerial Swarms – From Reliable Localization to Efficient Coordination.” Martin Saska concluded the workshop with his talk “Onboard relative localization for agile aerial swarming in the wild” about their work at the Czech TU in Prague. They also organize the Multi-robot systems summer school every year, so if you missed it this year, make sure to mark it in your calendar for next summer!
We had four tutorials in the middle of the workshop as well. Gábor also showed Skybrush in simulation after his talk for participants to try out. Additionally, we had tutorials that included real, flying Crazyflies live inside the workshop room! It was a bit of a challenge to set up due to the size of the room we were given, but with the lighthouse system it all worked out! Miguel and Rafael from Aerostack2 were first up, showing a leader-follower demo. Next up were Wolfgang and Kimberly (Crazyswarm2) who showed three Crazyflies collaboratively mapping the room, and finally, Andrea and Lorenzo from CrazyChoir demoed formation control in flight.
Luckily, there was also a bit of time to visit Eindhoven for a field trip to the 2024 edition of the world championship competitions of RoboCup! This is a very large robotics competition held in several different divisions, namely Soccer (with many subdivisions), Industrial, Rescue, @Home, and Junior. Each country usually has its own national championships, and those that win there can compete in the big leagues at events like these. RoboCup was extremely fun to attend, so if any robotics enthusiasts happen to live close to one of these, go! It’s awesome.
Photos of the field trip to RoboCup
Maybe drone competitions might be one of RoboCup’s divisions in the future :)
This week, we have a guest blog post from Scott at Droneblocks.
DroneBlocks is a cutting-edge platform that has transformed how educators worldwide enrich STEM programming in their classrooms. As pioneers in the EdTech space, DroneBlocks wrote the playbook on integrating drone technology into STEM curriculum for elementary, middle, and high schools, offering unparalleled resources for teaching everything from computer science to creative arts. What started as free block coding software and video tutorials has become a comprehensive suite of drone and robotics educational solutions. The Block-Coding software still remains free to all, as the DroneBlocks mission has always been to empower educators and students, allowing them to explore and lead the way. This open-source attitude set DroneBlocks on a mission to find the world’s best and most accessible micro-drone for education, and they found it in Sweden!
Previously, DroneBlocks had worked alongside drone juggernaut DJI and their Tello Drone. The Tello was a great tool for its time, but when DJI decided to discontinue it with little input from its partners and users, it made the break much easier. The hunt began for a DJI Tello replacement and an upgrade!
Bitcraze’s choice to build Crazyflie as an open platform had their drone buzzing wherever there was curiosity. The Crazyflie was developed to fly indoors, swarm, and be mechanically simplistic. DroneBlocks established that the ideal classroom micro-drone required similar characteristics. This micro-drone needed to be small for safety but sturdy for durability. It also needed to be easy to assemble and simple in structure for students new to drones. Most importantly, the ideal drone needed to have an open line of software communication to be fully programmable. Finally, there had to be an opportunity for a long-lasting partnership with the drone manufacturer, including government compliance.
After extensive searching and testing by DroneBlocks, the Crazyflie was a diamond in the rough – bite-sized and lightweight, supremely agile and accurate, reliable and robust, and most importantly, it was an open-source development platform. The DroneBlocks development team took the Crazyflie for a spin (or several) and with excitement, it was shared with the larger curriculum team to be mined for learning potential. It was promising to see Crazyflie’s involvement in university-level research studies, which proved it meant business. DroneBlocks knew the Crazyflie had a lot going for it – on its own. The team imagined how, when paired with DroneBlocks’ Block Coding software, Flight Simulator, and Curriculum Specialists, the Crazyflie could soar to atmospheric heights!
Hardware? Check. Software? Check. But what about compatibility? DroneBlocks was immediately drawn to the open communication and ease of conversation with the Bitcraze team. It was obvious that both Bitcraze and DroneBlocks were born from a common thread and shared a mutual goal: to empower people to explore, investigate, innovate, research, and educate.
DroneBlocks has since built a new Block Coding interface around the Crazyflie, allowing students to pilot their new drone autonomously and learn the basics of piloting and coding concepts. This interface is offered with a brand new drone coding simulator environment so students can test their code and fly the Crazyflie in a virtual classroom environment.
The Crazyflie curriculum currently consists of courses covering building, configuring, and finally, programming your drone with block coding (DroneBlocks) and Python. DroneBlocks’ expert curriculum team designed these courses to enable learners of all ages and levels to learn step by step through video series and exercises. New courses around block coding and Python are in constant development and will be continuously added to the DroneBlocks curriculum platform.
Crazyflie Drones now headline DroneBlocks’ premiere classroom launch kit. The DroneBlocks Autonomous Drones Level II kit encompasses everything a middle or high school would need to launch a STEM drone program, including the hardware, necessary accessories, and safety wear paired with the DroneBlocks software and curriculum. As a result, thousands of new students have entered the world of Drones and programming thanks to the Bitcraze + DroneBlocks partnership.
DroneBlocks has become an all-inclusive drone education partner for engaging and innovative learning experiences—and the Crazyflie delivers this by being a cutting-edge piece of hardware in a clever package.
Welcome to the “The Beginner’s Guide to Drones” for programmers curious about exploring the world of unmanned aerial vehicles (UAVs). If you’re a coder from another field, this guide will walk you through the basics of drones, their components, and how to start programming them. Let’s dive in and see how your coding skills can take flight!
If you come from an engineering field, you might already know the basics of some of these topics, however you might still have use of the overview and can use the resources to get more specific knowledge.
The Robotics part
First and foremost, you’ll need some basic robotics skills. We start of with the most basic question, “What is a Robot?”. A robot uses sensors to create an internal model of its environment, and actuators to act on/in its environment. The specifics of the internal model depend on the robot’s purpose, but a crucial component is understanding its location and orientation within that environment.
Linear algebra basics
To understand how a quadcopter perceives its environment and its own position, you’ll need some basic skills in linear algebra, particularly in matrices, vectors, and frame rotation. These skills are essential for comprehending the mathematical principles behind quadcopter navigation.
To build an internal model of its own movement and orientation, an Inertial Measurement Unit (IMU) is used. An IMU consists of a gyroscope, an accelerometer, and sometimes a magnetometer. These sensors, when combined using sensor fusion techniques (See “Control Theroy” below), help determine the quadcopter’s angular velocity and linear acceleration. This data allows the drone to calculate its orientation and movement.
Picture of a Crazyflie in relation to a reference frame from Bitcraze.io
The quadcopter can now determine its relationship to its starting position and the gravitational field. However, relying solely on an IMU tends to cause drift over time. Imagine trying to stand on one leg with your eyes closed—eventually, you’ll lose balance.
For improved stability, a drone often needs additional sensors, such as a camera, to help stabilize its position. Other sensor systems can also be used to determine relative or absolute position. While an IMU can sense changes in position relative to a starting point, an external positioning system is necessary for stability and obtaining absolute positions. This system acts as a reference frame for the drone.
Drones flying outdoors typically use GPS combined with RTCM, since it is available almost anywhere, ease to use, and has centimeter-level accuracy.
For indoor use, as with Crazyflies, the default used positioning system is motion-capture system but there are others as well. This area is at the cutting edge of science, with new technologies emerging constantly. However, many effective systems are available, though they may have constraints regarding power efficiency, flight area size, update speed, or precision.
This is an image of some common techniques for positioning. a) AOA, b) TOA, TWR c) TDOA taken from Researchgate.com
Control theory
Now that drone can understand its position and orientation in space, the next step is figuring out how to move within this space. Moving from point A to point B involves setting a “setpoint” and then determining how to use the drone’s actuators to reach this setpoint most efficiently. This is where control theory comes into play.
Drones generally use some sort of feedback control system, which in its most basic form looks something like this:
A very basic overview of a system with feedback control
In this system, the error (the difference between the current position and the setpoint) is fed back into the system to ensure the drone moves in a way that minimizes the error over time.
Various algorithms can calculate the best actuator output based on the error and current state. One of the most fundamental algorithms is the PID controller, which works well for linear systems. Understanding PID controllers requires some basic calculus, but the concept is straightforward. Here are some resources for simple explanations:
For IMUs, there is a particularly useful filter to know about, given its widespread use. The accelerometer and gyroscope each have their own profiles of noise and drift. The accelerometer is sensitive to short-term noise, while the gyroscope drifts slowly over time. To mitigate these issues, a combination of both measurements is often used. The complementary filter is ideal for this situation, leveraging the strengths of both sensors to correct the measurements effectively.
For more complex scenarios, advanced controllers like Kalman filters and others can be used. It’s also possible to combine multiple controllers to achieve better performance.
Drone actuators, primarily consisting of motors and propellers, are critical for controlling a drone’s movement and stability. The motors and propellers are typically called the “drive train” or “power train”. The motors used on drones are usually brushed or brushless DC motors. Propellers are attached to the motors and generate lift by pushing air downwards. The size, shape, and pitch of the propellers affect the drone’s performance, including speed, lift, and maneuverability. Together, the precise control of motors and propellers enables a drone to perform complex maneuvers, maintain stability, and achieve efficient flight.
Today we welcome Sam Schoedel and Khai Nguyenfrom Carnegie Mellon University. Enjoy!
We’re excited to share the research we’ve been doing on model-predictive control (MPC) for tiny robots! Our goal was to find a way to compress an MPC solver to a size that would fit on common microcontrollers like the Crazyflie’s STM32F405 while being fast enough to control the higher frequency dynamics of smaller robots. We came up with a few tricks to make that happen and dubbed the resulting solver TinyMPC. When it came time for hardware experiments, using the Crazyflie just made sense. A tiny solver deserves a tiny robot.
Motivation
Model predictive control is a powerful tool for controlling complex systems, but it is computationally expensive and thus often limited to use cases where the robot can either carry enough computational power or when offboard computing is available. The problem becomes challenging to solve for small robots, especially when we want to perform all of the computation onboard. Smaller robots have inherently faster dynamics which require higher frequency controllers to stabilize, and because of their size they don’t have the capacity to haul around as much computational power as their larger robot counterparts. The computers they can carry are often highly memory-constrained as well. Our question was “how can we shrink the computational complexity and memory costs of MPC down to the scale of tiny robots?”
What We Did
We settled on developing a convex model predictive control solver based on the alternating direction method of multipliers. Convex MPC solvers are limited to reasoning about linear dynamics (on top of any other convex constraints), but have structure that TinyMPC exploits to solve problems efficiently. The tricks we used to achieve this efficiency are described in the paper, but it boils down to rewriting the problem as a constrained linear-quadratic regulator to reduce the memory footprint and then precomputing as many matrices as possible offline so that online calculations are less expensive. These tricks allowed us to fit long-time horizon MPC problems on the Crazyflie and solve them fast enough for real-time use.
What TinyMPC Can Do
We decided to demonstrate the constraint-handling capabilities of TinyMPC by having the Crazyflie avoid a dynamic obstacle. We achieved this by re-computing hyperplane constraints (green planes in the first video) about a spherical obstacle (transparent white ball) for each knot point in the trajectory at every time step, and then by solving the problem with the new constraints assuming they stayed fixed for the duration of the solve.
In the two videos below, the reference trajectory used by the solver is just a hover position at the origin for every time step. Also, the path the robot takes in the real world will never be exactly the same as the trajectory computed by the solver, which can easily result in collisions. To avoid this, we inflated the end of the stick (and the simulated obstacle) to act as a keep-out region.
TinyMPC is restricted to reasoning about linear dynamics because of its convex formulation. However, a simple linearization can be taken pretty far. We experimented with recovering from different starting conditions to push the limits of our linear Crazyflie model and were able to successfully recover from a 90 degree angle while obeying the thrust commands for each motor.
We recently added support for second-order cone constraints as well. These types of constraints allow TinyMPC to reason about friction and thrust cones, for example, which means it can now intelligently control quadrupeds on slippery surfaces and land rockets. To clearly demonstrate the cone constraint, we took long exposure photos of the Crazyflie tracking a cylindrical landing trajectory without any cone constraints (red) and then with a spatial cone constraint that restricts the landing maneuver to a glide slope (blue).
How To Use TinyMPC
All of the information regarding the solver can be found on our website and GitHub org (which is where you can also find the main GitHub repository). TinyMPC currently has a Python wrapper that allows for validating the solver and generating C++ code to run on a robot, and we have a few examples in C++ if you don’t want to use Python. Our website also explains how to linearize your robot and has some examples for setting up the problem with a linear model, solving it an MPC loop, and then generating and running C++ code.
Most importantly to the Crazyflie community, our TinyMPC-integrated firmware is available and should work out of the box. Let us know if you use it and run into issues!
Our accompanying research papers:
Khai Nguyen, Sam Schoedel, Anoushka Alavilli, Brian Plancher, and Zachary Manchester. “TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers.” arXiv preprint arXiv:2310.16985 (2023). https://arxiv.org/pdf/2310.16985
Sam Schoedel, Khai Nguyen, Elakhya Nedumaran, Brian Plancher, and Zachary Manchester. “Code Generation for Conic Model-Predictive Control on Microcontrollers with TinyMPC.” arXiv preprint arXiv:2403.18149 (2024). https://arxiv.org/pdf/2403.18149
We would love your feedback and suggestions, and let us know if you use TinyMPC for your tiny platforms!
We are happy to announce the latest updates to the Crazyflie client and Python library. Major changes include improved persistent parameter management, enhanced plotting with new x-axis manipulation features, and new default logging configurations (for PID tuning). Minor updates include bug fixes and documentation improvements.
Updated plotter tab. Besides the existing option for a number of samples, users can now set x-axis limits to a number of seconds or a time range.Updated parameters tab. Users can now mass dump/load persistent parameters to/from a file, or clear all stored persistent parameters.
Whenever we show the Crazyflie at our booth at various robotics conferences (like the recent ICRA Yokohama), we sometimes get comments like ‘ahh that’s cute’ or ‘that’s a fun toy!’. Those who have been working with it for their research know differently, but it seems that the general robotics crowd needs a little bit more… convincing! Disregarding its size, the Crazyflie is a great tool that enables users to do many awesome things in various areas of robotics, such as swarm robotics and autonomy, for both research and education.
We will be showing that off by giving a live tutorial and demonstration at the Robotics Developer Day 2024, which is organized by The Construct and will take place this Friday, 5th of July. We have a discount code for you to use if you want to get a ticket; scroll down for details. The code can be used until 12 am midnight (CEST) on the 2nd of July.
The Construct and Robotics Developer Day 2024
So a bit of background information: The Construct is an online platform that offers various courses and curriculums to teach robotics and ROS to their users. Along with that, they also organize all kinds of live training sessions and events like the Robotics Developer Day and the ROS Awards. Unfortunately, the deadline for voting in the latter has passed, but hopefully in the future, the Crazyflie might get an award of its own!
What stands out about the platform is its implementation of web-based virtual machines, called ‘ROSJects,’ where ROS and everything needed for it is already set up from the start. Anyone who has worked with ROS(2) before knows that it can be a pain to switch between different versions of ROS and Gazebo, so this feature allows users to keep those projects separate. For the ROS Developer Day, there will be about five live skill-learning sessions where a ROSject is already preconfigured and set up for the attendees, enabling them to try the tutorial simultaneously as the teacher or speaker explains the framework.
Skill learning session with the Crazyflie
One of the earlier mentioned skill learning sessions is, of course, one with the Crazyflie! The title is “ROS 2 with a Tiny Quadcopter,” and it is currently planned to be the first skill learning session of the event, scheduled at 15:15 (3:15 pm) CEST. The talk will emphasize the use of simulation in the development process with aerial robotics and iterating between the real platform and the simulated one. We will demonstrate this with a Crazyflie 2.1 equipped with a Lighthouse deck and a Multi-ranger deck. Moreover, it will also use a Qi-charging deck on a charging platform while it patiently waits for its turn :D
What we will be showing is a simple implementation of a mapping algorithm made specifically for the Crazyflie’s Multiranger deck, which we have demonstrated before at ROSCon Kyoto and in the Crazyswarm2 tutorials. What is especially different this time is that we are using Gazebo for the simulation parts, which required some skill learning on our side as we have been used to Webots over the last couple of years (see our tutorial for that). You can find the files for the simulation part in this repository, but we do advise you to follow the session first.
You can, if you want, follow along with the tutorial using a Crazyflie yourself. If you have a Crazyflie, Crazyradio, and a positioning deck (preferably Lighthouse positioning, but a Flowdeck would work as well), you can try out the real-platform part of this tutorial. You will need to install Crazyswarm2 on a separate Ubuntu machine and add a robot in your ROSject as preparation. However, this is entirely optional, and it might distract you from the cool demos we are planning to show, so perhaps you can try this as a recap after the actual skill learning session ;).
Here is a teaser of what the final stage of the tutorial will look like:
Win a lighthouse explorer bundle and a Hands-On Pass discount
We are also sponsors of the event and have agreed with The Construct to award one of the participants a Crazyflie if they win any contest. Specifically, we will be awarding a Lighthouse Explorer bundle, with a Qi deck and a custom-made charging pad similar to the ones we show at fairs like ICRA this year. So make sure to participate in the contests during the day for a chance to win this or any of the other prizes they have!
It is possible to follow the event for free, but if you’d like to participate with the ROSjects, you’ll need to get a hands-on pass. If you haven’t yet gotten a hands-on ticket for the Robotics Developer Day, please use our 50% off discount code:
On a side note, we will be at the Robotics: Science and Systems Conference in Delft from July 15th to 19th, 2024—just about two weeks from now. We won’t have a booth as we usually do, but we will be co-organizing a half-day workshop titled Aerial Swarm Tools and Applications (more details on this website).
We will be organizing this workshop together with our collaborators at Crazyswarm2, as well as the developers of CrazyChoir and Aerostack2. We’re excited to showcase demos of these frameworks with a bunch of actual Crazyflies during the workshop, if the demo gods are on our side :D. We will also have great speakers, including: SiQi Zhou (TU Munich), Martin Saska (Czech Technical University), Sabine Hauert (University of Bristol), and Gábor Vásárhelyi (Collmot/Eötvös University).
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. https://doi.org/10.3390/data9060079
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.
Two weeks ago, Arnaud, Barbara and Rik were at ICRA 2024 in Yokohama. At our booth we showed our current products as well as the upcoming brushless Crazyflie and the camera deck prototype.
As usual, the conference has been very busy with a lot of visitors and a lot of very interesting discussion. Thanks to everyone that passed by the booth, we have come back to Sweden with a lot of energy and new ideas!
The autonomous lighthouse swarm demo demo has worked pretty well. If you are interested to know more about it you can visit our event page. It is an autonomous demo with 3 brushless Crazyflies and 6 Crazyflie 2.1s flying autonomously. With the extended battery life of the brushless, we could operate the demo pretty much continuously.
If you’ve been at the conference, you may have spotted someone proudly sporting our exclusive ‘Bitcraze took my poster’ button. We’re excited to have received posters covering a wide range of topics, the walls of our office are eagerly awaiting these visual representations of your hard work and dedication. Thank you to everyone who has contributed.
