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From Shadows to Light: A swarm robotics approach with onboard control for seeking dynamic sources in constrained environments

  2024-11-11  |     |    Leave a comment

Today, we’re excited to share research from Vrije Universiteit Amsterdam, ‘From Shadows to Light,’ which presents an innovative swarm robotics approach where nano-drones autonomously track dynamic sources indoors.

Motivation

In dynamic and unpredictable indoor environments, locating moving sources—such as heat, gas, or light—presents unique challenges. GPS-denied settings, in particular, demand innovative and efficient onboard solutions for both control and sensing. Our research demonstrates how small drones, like Crazyflies, can be organized into a coordinated swarm to autonomously locate and follow these sources indoors, relying solely on onboard sensing and communication capabilities. Without sharing individual measurements, each drone adapts its behavior in response to its own sensor readings, allowing the swarm to collectively converge on the center of a light source through modified interactions with nearby agents.

Tugay Alperen (right) and Victor Retamal (left) during ICRA 2024 poster session

Method

Our approach enables each Crazyflie to function autonomously, using onboard sensing combined with continuous inter-agent communication at a frequency of 20 Hz. This methodology is structured around three core components:

Proximal Control and Collective Motion

Each drone broadcasts its position to nearby agents, enabling the calculation of relative positions to maintain safe distances. This proximal control ensures cohesive group movement by computing virtual force vectors for velocity commands, which are sent to onboard controllers operating at 20 Hz.

Source Seeking Through Adaptive Social Proximity

Drones use custom light sensors to detect local light intensity. Instead of directly adjusting positions based on this measurement, each drone modifies its social proximity to neighbors according to the sensed intensity without broadcasting this information. This adaptation allows the swarm to collectively follow the light gradient toward the source in a decentralized manner.

Obstacle Avoidance

Equipped with time-of-flight sensors, each drone independently detects obstacles and adjusts its trajectory to maintain safety. This ensures the swarm remains intact while navigating toward the source.

By combining continuous relative positioning, virtual force-based control, individual sensing, and adaptive social behavior, our methodology provides a robust framework for efficient source seeking in GPS-denied indoor environments.

Experimental Setup

Crazyflie equipped with Flow Deck v2, UWB Deck, Multi-Ranger Deck, and a custom-made deck that produces an analog voltage reading from an LDR for light intensity measurements.

The system architecture allowing us to achieve autonomous flocking and source localization with a swarm of Crazyflie

Our experiments take place in a 7×4.75-meter indoor arena with remotely controlled overhead light bulbs. These bulbs, activated individually or in pairs, create a moving light gradient. We tested our flocking swarm by initially positioning them at the edge of an illuminated area. As the light source shifted, we assessed the swarm’s performance by comparing their trajectories with the known centers of the illuminated areas without waiting for full convergence at each step. We also mapped our environment’s light intensity by moving a single Crazyflie randomly around the flight arena and recording the measurements to later merge on a single map to generate this light intensity heatmap.

The brightness values around the test environments, measured for each light source when only it was active.

Results

The flock flies as an ordered swarm, successfully localizing around the source with the swarm’s centroid positioned at the source center. (The centroid appears as a point without an arrow in the video.)

Even with an obstacle present within or between the illuminated regions, the flock successfully localizes around the center, avoiding the obstacle and maintaining order and cohesion within the swarm. The Multi-Ranger deck provides distance measurements for obstacle detection.

Future Directions

As the next step, we plan to apply our highly generalizable algorithm to various source types, including gas sources, radio signals, and similar sources that provide only scalar strength measurements rather than directional cues. Additionally, we have demonstrated that our flocking and source localization algorithms work effectively in 3D. We aim to showcase a fully functional application with a 3D-localized source and a flocking swarm operating in 3D space. Finally, we are working toward achieving fully onboard relative localization, which would eliminate the need for any indoor positioning system. This advancement would allow our swarm to operate autonomously in any environment, replicating the same behavior wherever it is deployed.

Links

The authors were with the Vrije Universiteit Amsterdam.

Please feel free to contact us with any questions or ideas: t.a.karaguzel@vu.nl

Please cite this as:

@ARTICLE{10314746,
  author={Karagüzel, Tugay Alperen and Retamal, Victor and Cambier, Nicolas and Ferrante, Eliseo},
  journal={IEEE Robotics and Automation Letters}, 
  title={From Shadows to Light: A Swarm Robotics Approach With Onboard Control for Seeking Dynamic Sources in Constrained Environments}, 
  year={2024},
  volume={9},
  number={1},
  pages={127-134},
  keywords={Robot sensing systems;Autonomous aerial vehicles;Position measurement;Vehicle dynamics;Sensors;Location awareness;Drones;Swarm robotics;aerial systems: perception and autonomy;multi-robot systems},
  doi={10.1109/LRA.2023.3331897}}

New research video compilation

  2024-10-07  |     |    Leave a comment

It’s now become a tradition to create a video compilation showcasing the most visually stunning research projects that feature the Crazyflie. Since our last update, so many incredible things have happened that we felt it was high time to share a fresh collection.

As always, the toughest part of creating these videos is selecting which projects to highlight. There are so many fantastic Crazyflie videos out there that if we included them all, the final compilation would last for hours! If you’re interested, you can find a more extensive list of our products used in research here.

The video covers 2023 and 2024 so far. We were once again amazed by the incredible things the community has accomplished with the Crazyflie. In the selection, you can see the broad range of research subjects the Crazyflie can be a part of. It has been used in mapping, or swarms – even in heterogeneous swarms! With its small size, it has also been picked for human-robot interaction projects (including our very own Joseph La Delfa showcasing his work). And it’s even been turned into a hopping quadcopter!

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

But enough talking, the best way to show you everything is to actually watch the video:

A huge thank you to all the researchers we reached out to and who agreed to showcase their work! We’re especially grateful for the incredible footage you shared with us—some of it was new to us, and it truly adds to the richness of the compilation. Your contributions help highlight the fantastic innovations happening within the Crazyflie community. Let’s hope the next compilation also shows projects with the Brushless!

RSS conference and Aerial Swarm Workshop

  2024-08-05  |     |    Leave a comment

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.

You can find all the talks, demos, and papers on the website of RSS 2024

Photos of day 3 of RSS

Aerial Swarm Workshop

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.

You can see the Crazyflies demos flying during the tutorials in the video below. The recording of each of the talks can be found on the workshops website: https://imrclab.github.io/workshop-aerial-swarms-rss2024/

RoboCup 2024 Eindhoven

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 :)

Bringing Model Predictive Control to the Crazyflie with TinyMPC

  2024-07-15  |     |    2 Comments

Today we welcome Sam Schoedel and Khai Nguyen from 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!

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

  2024-06-24  |     |    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:

  1. 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.
  2. 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.
  3. 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.
  4. 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

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.

ICRA 2024 POSTERS

  2024-06-10  |     |    Leave a comment

As we mentioned earlier, ICRA Yokohama was full of exciting encounters – we really enjoyed meeting researchers, tech companies, and enthusiastic roboticists during those 4 days.

One challenge was to bring back as many research posters featuring the Crazyflies as possible. The goal was to decorate the walls of the office with them, as a “hall of fame”. And I’m really, really proud to show you how it turned up!

This was before
And this is now!

In total, we received 6 new posters. Here they are:

Optimal Collaborative Transportation for Under-Capacitated Vehicle Routing Problems using Aerial Drone swarms
Akash Kopparam Sreedhara, Deepesh Padala, Shashank Mahesh, Kai Cui, Mengguang Li, Heinz Koeppl

This paper presents a strategy for optimizing the collaborative transportation of payloads in an under-capacitated vehicle routing scenario. The Crazyflies work together to dynamically adjust routes based on real-time data and transport capacities, and collaborate to lift and transport heavier payloads.

From Shadows to Light: A Swarm Robotics Approach With Onboard Control for Seeking Dynamic Sources in Constrained Environments
T. A. Karagüzel, V. Retamal, N. Cambier and E. Ferrante

The paper describes a method for enabling a swarm of Crazyflies to dynamically seek and locate a moving target or source in constrained, GNSS-denied environments. Using a simple rule-based approach, the drones track dynamic source gradients and navigate obstacles autonomously with fully onboard systems.

CrazySim: A Software-in-the-Loop Simulator for the Crazyflie Nano Quadrotor
Christian Llanes, Zahi Kakish, Kyle Williams, and Samuel Coogan

We actually already have a blogpost presenting this paper, and we’re so happy to have it represented in our office now!

TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers
Khai Nguyen, Sam Schoedel, Anoushka Alavilli, Brian Plancher, Zachary Manchester

The paper presents TinyMPC, a high-speed model-predictive control (MPC) solver designed for resource-constrained microcontrollers on small robots like the Crazyflie. TinyMPC efficiently handles real-time trajectory tracking and dynamic obstacle avoidance, outperforming traditional solvers.

Robust and Efficient Depth-Based Obstacle Avoidance for Autonomous Miniaturized UAVs
H. Müller, V. Niculescu, T. Polonelli, M. Magno and L. Benini

The paper introduces a lightweight obstacle avoidance system for nano quadcopters, leveraging a novel 64-pixel multizone time-of-flight (ToF) sensor to safely and effectively navigate complex indoor environments. Tested on the Crazyflie 2.1, the system achieves 100% reliability at a speed of 0.5 m/s, all while using only 0.3% of the onboard processing power, demonstrating its suitability for autonomous operations in unexplored settings.

Fully onboard Low-power localization with semantic sensor fusion on a Nano-UAV using floor plans
Nicky Zimmerman, Hanna Müller, Michele Magno, Luca Benini

This paper introduces a method for autonomous localization in nano-sized UAVs like the Crazyflie by fusing geometric data from time-of-flight sensors with semantic information extracted from images. The approach leverages annotated floor plans to improve navigation accuracy without adding extra deployment costs. The system operates efficiently with limited onboard computational resources, achieving a 90% success rate in real-world office environments.

A big thanks, once again, to all of those who gave us their posters!

Come see us at ICRA 2024 Yokohama!

  2024-05-13  |     |    Leave a comment

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

The gang before the rush starts!

Crazyflie Brushless and Camera expansion

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

The Crazyflie Brushless flying with a Camera deck.

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

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

Hand in your Crazyflie posters at our booth!

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

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

The ‘Bitcraze took my poster’ button!

ICRA 2024 and other news

  2024-04-22  |     |    Leave a comment

ICRA Yokohama

From the beginning of the company, we’ve always loved to join in at conferences. Only at a conference do you get the opportunity to show our products, meet our users or other tech-oriented people, learn about what others are doing, and let’s not forget the chance to discover a new place!

This year, we’ll be present at ICRA Yokohama – it’s in just 3 weeks. We’ll have a booth there (IC085 if you’re looking for us). We’ll be showing our autonomous demo with a twist just like we have shown last time, so please check the event page. This demo is extremely impressive and we’ve been improving on it each time we’ve shown it – beginning in our latest Japan trip and lastly at the last ICRA too. What’s new?

We’re really excited to be showing that and receive feedback, but also in hearing about what our users have been doing. ICRA is always a perfect place to catch up on all the amazing papers and publications featuring our hardware, and we couldn’t be prouder of all the cool stuff we’ve seen so far. We’re so proud, in fact, that we want to be able to show off! So, if you have a paper or a publication featured at ICRA, let us know – you can write us an email at contact@bitcraze.io, leave a comment below this post, or pass by our booth.

In fact, we’re prepared to make a deal. If you have a nice poster featuring our products and don’t know what to do with it once you’ve presented it, pass by our booth! We’re ready to swap them for something extra special. We plan to have a “hall of fame” at the office featuring your awesome work – in fact, it’s an idea we had last ICRA when someone just offered us their posters. Now, we’d like to cover our walls with them!

The corridor leading to the kitchen – we have space to show off the awesomeness!

So, whether you’re a seasoned conference-goer or a first-time attendee, don’t hesitate to wsing by our booth, say hello, and discover our newest demo! We hope to see you there.

Dev meeting

Next developer meeting is going to be on the 8th of May – we traditionally have a dev meeting every first Wednesday of the month, but this time it happens to be on the 1st of May which is a holiday here in Sweden. So already prepare your calendar for the 8th of May at 15.00 CET, and stay tuned for more info on which topic we’ll talk about!

Crazyflies back in stock !

You may have noticed that the Crazyflies have been out of stock for some time now. After some adventures, we are now fully back in stock with most of our bundles and products available in the shop!

CrazySim: A Software-in-the-Loop Simulator for the Crazyflie

  2024-04-15  |     |    Leave a comment

This week we have a guest blogpost by Christian Llanes, a Robotics PhD of from Formal Methods & Autonomous Control of Transportation Systems Lab of the Georgia Institute of Technology. Enjoy!

Why do we need simulators?

Simulators are one of the most important tools used in robotics research. They usually are designed for different purposes with different levels of complexity. For example, simulators with low computational overhead that are parallelizable are mainly used for either training reinforcement learning algorithms or Monte Carlo sampling for verification of task completion in a nondeterministic environment. Some simulators also use rendering engines for the graphical display of models and the environment or when cameras are intended to be used in the robotics platform. Simulation is also useful for the development and deployment of new robotics firmware features where the firmware is compiled on a test machine and run in the loop with a simulated sensor suite. This simulator configuration is known as software-in-the-loop (SITL) because the vehicle firmware is intended to be run in the loop with the simulated vehicle physics and/or rendering engine. This feature is supported by autopilot suites such as PX4, ArduPilot, CogniPilot, and BetaFlight. This feature is not officially supported yet for Crazyflies because it requires a large overhaul of the firmware to be able to compile on a desktop machine and interact with different simulators such as Gazebo, Webots, PyBullet, CoppeliaSim, Isaac Sim, or Unreal Engine.

CrazySim

Last summer I began working with Crazyflies and noticed this Crazyflie simulator gap. I stumbled on a community-developed project for Crazyflie SITL called sim_cf. This project is exactly what I was looking for. However, the firmware used by the project is from July 2019 and the official firmware has had over 2000 commits made since then. The project also uses ROS 1, Gazebo Classic, and doesn’t support the Crazyflie Python library (CFLib). Using this project as a starting point I set out to develop CrazySim–a Crazyflie SITL project that doesn’t require ROS, uses Gazebo Sim, and supports connectivity through CFLib. Using CFLib we can connect the simulator to external software such as Crazyswarm2 or the Crazyflie ground station client. Users test their control algorithms in the external software using the simulator interface before deploying to real flight hardware.

An example of offboard model predictive control design and deployment workflow using CrazySim.

Using the Crazyflie Client for PID Tuning

We have also provided a modified Crazyflie client for CrazySim support. The Crazyflie client is a cool tool for testing a single drone in hardware. We can perform command based flight control, look at real time plots, save log data, and tune PID values in real time. The PID values are typically tuned for an out of the box Crazyflie. However, when we modify the Crazyflie and add extra weight through batteries, decks, and upgraded thrust motors then the behavior of the Crazyflie will change. If a user wants to tune a custom Crazyflie setup, then they can add additional models in this folder with their own motor and mass properties. Then they just need to add it to the list of supported models in either of the launch scripts. There is already an example model for the thrust upgrade bundle. Documentation for installing the custom client can be found here.

PID tuning a simulated Crazyflie using CrazySim on the Crazyflie PC client.

Crazyswarm2

We can now connect to the simulated Crazyflie firmware using CFLib. Therefore, we can set up a ROS 2 interface through Crazyswarm2 for swarm command and control through ROS 2 topics and services. To do this we first startup the drones using any of the launch scripts. 

bash tools/crazyflie-simulation/simulator_files/gazebo/launch/sitl_multiagent_square.sh -n 16 -m crazyflie

Then, we bring up Crazyswarm2 after setting up the configuration file for the number of drones chosen.

ros2 launch crazyflie launch.py backend:=cflib

We demonstrate an example of how we can control a swarm of drones using Crazyswarm2 GoTo service commands.

Crazyswarm2 GoTo service commands using CrazySim.

ICRA 2024

CrazySim is also being presented as a paper at the 2024 IEEE International Conference on Robotics and Automation in Yokohama, Japan. If you are attending this conference and are interested in this work, then I invite you to my presentation and let me know that you are coming from this blog post after. For the paper, I created a multi agent decentralized model predictive controller (MPC) case study on ROS 2 to demonstrate the CrazySim simulation to hardware deployment workflow. Simulating larger swarms with MPC may require a high performance computer. The simulations in this work were performed on an AMD Ryzen 9 5950X desktop processor.

Model predictive control case study for ICRA 2024 paper.

Links

  1. CrazySim
  2. Modified Crazyflie client

Other Crazyflie SITL projects:

  1. sim_cf
  2. sim_cf2 blog post
  3. LambdaFlight blog post

Adaptive Cooperative Flight for Transportation using Crazyflies

  2023-11-13  |     |    Leave a comment

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

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

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

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

We first tested with some symmetric triangle and quad formations.

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

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

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

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

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

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

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

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

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