Category: Research

As you may have noticed from the recent blog posts, we were very excited about ICRA London 2023! And it seems that we had every right to be, as this conference had the highest number of Crazyflie related papers compared to all the previous robotics conferences! In the past, the conferences typically had between 13-16 papers, but this time… BOOM! 28 papers! In this blog post, we will provide a list of these papers and give a general evaluation of the topics and themes covered so far.

So here some stats:

  • ICRA had 1655 papers accepted (43 % acceptance rate)
  • 28 Crazyflie papers (25 proceedings, 1 RA-L, 1RO-L, 1 late breaking result postor)
    • Haven’t included the workshop papers this time (no time)
  • The major topics we discovered were swarm coordination, safe trajectory planning, efficient autonomy, and onboard processing

Additionally, we came across a few notable posters, including one about a grappling hook for the Crazyflie [26], a human suit that allows for drone control [5], the Bolt made into a monocopter with a Jetson companion [16], and a flexible fixed-wing platform driven by a barebone Crazyflie [1]. We also observed a growing interest in aerial robotics with approximately 10% of all sessions dedicated to UAVs. Interestingly, 18 out of the 28 Crazyflie papers were presented in non-UAV specialized sessions, such as multi-robot systems and vision-based navigation.

Swarm coordination

Swarms were a hot topic at ICRA 2023 as already noticed by this tweet of Ramon Roche. We had over 10 papers dedicated to this topic, including one that involved 16 Crazyflies [9]. Surprisingly, more than half of the papers utilized multiple Crazyflies. This already sets a different landscape compared to IROS 2022, where autonomous navigation took center stage.

In IROS 2022, we witnessed single-drone gas mapping using a Crazyflie, but now it has been replicated in the Webots simulation using 2 Crazyflies [23]. Does this imply that we might witness a 3D gas localizing swarm at IROS 2023? We can’t wait.

Furthermore, we came across a paper [11] featuring the Bolt-based platform, which demonstrated flying formations while being attached to another platform using a string. It presented an intriguing control problem. Additionally, there was a work that combined safe trajectory planning with swarm coordination, enabling the avoidance of obstacles and people [12]. Moreover, there were some notable collaborations, such as robot pickup and delivery involving the Turtlebot 3 Burger [22].

Given the abundance of swarm papers, it’s impossible for us to delve into each of them, but it’s all very impressive work.

Safe trajectory planning and AI-deck

Another significant buzzword at ICRA was “safety-critical control.” This is important to ensuring safe control from a human interface [15] and employing it to facilitate reinforcement learning [27]. The latter approach is considered less “safe” in terms of designing controllers, as evidenced by the previous IROS competition, the Safe Robot Learning Competition. Although the Crazyflie itself is quite safe, it makes sense to first experiment with safe trajectories on it before applying them to larger drones.

Furthermore, we encountered approximately three papers related to the AIdeck. These papers covered various topics such as optical flow detection [17], visual pose estimation [21], and the detection of other Crazyflies [5]. During the conference, we heard that the AIdeck presents certain challenges for researchers, but we remain hopeful that we will see more papers exploring its potential in the future!

List of papers

This list not only physical Crazyflie papers, but also papers that uses simulation or parameters of the Crazyflie. This time the workshop papers are not included but we’ll add them later once we have the time

Enjoy!

  1. ‘A Micro Aircraft with Passive Variable-Sweep Wings’ Songnan Bai, Runze Ding, Pakpong Chirarattananon from City University of Hong Kong
  2. ‘Onboard Controller Design for Nano UAV Swarm in Operator-Guided Collective Behaviors’ Tugay Alperen Karagüzel, Victor Retamal Guiberteau, Eliseo Ferrante from Vrije Universiteit Amsterdam
  3. ‘Multi-Target Pursuit by a Decentralized Heterogeneous UAV Swarm Using Deep Multi-Agent Reinforcement Learning’ Maryam Kouzehgar, Youngbin Song, Malika Meghjani, Roland Bouffanais from Singapore University of Technology and Design [Video]
  4. ‘Inverted Landing in a Small Aerial Robot Via Deep Reinforcement Learning for Triggering and Control of Rotational Maneuvers’ Bryan Habas, Jack W. Langelaan, Bo Cheng from Pennsylvania State University [Video]
  5. ‘Ultra-Low Power Deep Learning-Based Monocular Relative Localization Onboard Nano-Quadrotors’ Stefano Bonato, Stefano Carlo Lambertenghi, Elia Cereda, Alessandro Giusti, Daniele Palossi from USI-SUPSI-IDSIA Lugano, ISL Zurich [Video]
  6. ‘A Hybrid Quadratic Programming Framework for Real-Time Embedded Safety-Critical Control’ Ryan Bena, Sushmit Hossain, Buyun Chen, Wei Wu, Quan Nguyen from University of Southern California [Video]
  7. ‘Distributed Potential iLQR: Scalable Game-Theoretic Trajectory Planning for Multi-Agent Interactions’ Zach Williams, Jushan Chen, Negar Mehr from University of Illinois Urbana-Champaign
  8. ‘Scalable Task-Driven Robotic Swarm Control Via Collision Avoidance and Learning Mean-Field Control’ Kai Cui, MLI, Christian Fabian, Heinz Koeppl from Technische Universität Darmstadt
  9. ‘Multi-Agent Spatial Predictive Control with Application to Drone Flocking’ Andreas Brandstätter, Scott Smolka, Scott Stoller, Ashish Tiwari, Radu Grosu from Technische Universität Wien, Stony Brook University, Microsoft Corp, TU Wien [Video]
  10. ‘Trajectory Planning for the Bidirectional Quadrotor As a Differentially Flat Hybrid System’ Katherine Mao, Jake Welde, M. Ani Hsieh, Vijay Kumar from University of Pennsylvania
  11. ‘Forming and Controlling Hitches in Midair Using Aerial Robots’ Diego Salazar-Dantonio, Subhrajit Bhattacharya, David Saldana from Lehigh University [Video]
  12. ‘AMSwarm: An Alternating Minimization Approach for Safe Motion Planning of Quadrotor Swarms in Cluttered Environments’ Vivek Kantilal Adajania, Siqi Zhou, Arun Singh, Angela P. Schoellig from University of Toronto, Technical University of Munich, University of Tartu [Video]
  13. ‘Decentralized Deadlock-Free Trajectory Planning for Quadrotor Swarm in Obstacle-Rich Environments’ Jungwon Park, Inkyu Jang, H. Jin Kim from Seoul National University
  14. ‘A Negative Imaginary Theory-Based Time-Varying Group Formation Tracking Scheme for Multi-Robot Systems: Applications to Quadcopters’ Yu-Hsiang Su, Parijat Bhowmick, Alexander Lanzon from The University of Manchester, Indian Institute of Technology Guwahati
  15. ‘Safe Operations of an Aerial Swarm Via a Cobot Human Swarm Interface’ Sydrak Abdi, Derek Paley from University of Maryland [Video]
  16. ‘Direct Angular Rate Estimation without Event Motion-Compensation at High Angular Rates’ Matthew Ng, Xinyu Cai, Shaohui Foong from Singapore University of Technology and Design
  17. ‘NanoFlowNet: Real-Time Dense Optical Flow on a Nano Quadcopter’ Rik Jan Bouwmeester, Federico Paredes-valles, Guido De Croon from Delft University of Technology [Video]
  18. ‘Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning’ Cheng Liu, Erik-jan Van Kampen, Guido De Croon from Delft University of Technology
  19. ‘Relay Pursuit for Multirobot Target Tracking on Tile Graphs’ Shashwata Mandal, Sourabh Bhattacharya from Iowa State University
  20. ‘A Distributed Online Optimization Strategy for Cooperative Robotic Surveillance’ Lorenzo Pichierri, Guido Carnevale, Lorenzo Sforni, Andrea Testa, Giuseppe Notarstefano from University of Bologna [Video]
  21. ‘Deep Neural Network Architecture Search for Accurate Visual Pose Estimation Aboard Nano-UAVs’ Elia Cereda, Luca Crupi, Matteo Risso, Alessio Burrello, Luca Benini, Alessandro Giusti, Daniele Jahier Pagliari, Daniele Palossi from IDSIA USI-SUPSI, Politecnico di Torino, Università di Bologna, University of Bologna, SUPSIETH Zurich [Video]
  22. ‘Multi-Robot Pickup and Delivery Via Distributed Resource Allocation’ Andrea Camisa, Andrea Testa, Giuseppe Notarstefano from Università di Bologna [Video]
  23. ‘Multi-Robot 3D Gas Distribution Mapping: Coordination, Information Sharing and Environmental Knowledge’ Chiara Ercolani, Shashank Mahendra Deshmukh, Thomas Laurent Peeters, Alcherio Martinoli from EPFL
  24. ‘Finding Optimal Modular Robots for Aerial Tasks’ Jiawei Xu, David Saldana from Lehigh University
  25. ‘Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systems’ Craig Knuth, Glen Chou, Jamie Reese, Joseph Moore from Johns Hopkins University, MIT
  26. ‘Spring-Powered Tether Launching Mechanism for Improving Micro-UAV Air Mobility’ Felipe Borja from Carnegie Mellon university
  27. ‘Reinforcement Learning for Safe Robot Control Using Control Lyapunov Barrier Functions’ Desong Du, Shaohang Han, Naiming Qi, Haitham Bou Ammar, Jun Wang, Wei Pan from Harbin Institute of Technology, Delft University of Technology, Princeton University, University College London [Video]
  28. ‘Safety-Critical Ergodic Exploration in Cluttered Environments Via Control Barrier Functions’ Cameron Lerch, Dayi Dong, Ian Abraham from Yale University

We are excited to announce that we will be having developer meetings on first Wednesdays of every month! Additionally, we are thrilled to be present in person at ICRA 2023 in London. During the same conference, there will be half day workshop called ‘The Role of Robotics Simulators for Unmanned Aerial Vehicles’ so make sure to sign-up! Please check out our newly updated event-page !

Monthly Developer meetings

We have had some online developer meetings in the past covering various topics. While these meetings may not have been the most popular, we believe it is crucial to maintain communication with the community and have interesting discussions, and exchange of ideas. However, we used to plan them ad-hoc and we had no regularity in them, which sometimes caused some of us **cough** especially me **cough**, to create confusion about the timing and location. To remove these factors of templexia (dyslexia for time), we will just have it simply on the first Wednesday of every month.

So our first one with be on Wednesday 5th of April at 15:00 CEST and the information about the particular developer meeting will be as usual on discussions. From 15:00 – 15:30 it will be a general discussion, probably with a short presentation, about a topic to be determined. From 15:30-16:00 will address regular support question from anybody that need help with their work on the Crazyflie.

ICRA 2023 London

ICRA will be held in London this year, from May 29 – June 2nd, atthe ExCel venue. We will be located in the H11 booth in the exhibitor hall, but as the date approaches, we will share more about what awesome prototypes we will showcase and what we will demonstrate on-site. Rest assured, plenty of Crazyflies will be flown in the cage! To get an idea of what we demo-ed last year it IROS Kyoto, please check out the IROS 2022 event page. Matej from Flapper Drones will join us at our booth to showcase the Flapper drone.

We are thinking of organizing a meetup for participants on the Wednesday after the Conference Dinner, so we will share the details of that in the near future as well. Also keep an eye on our ICRA 2023 event page for updated information.

ICRA Aerial Robotic Simulation Workshop

I (Kimberly) will also be present at the ‘The Role of Robotics Simulators for Unmanned Aerial Vehicles‘ workshop on Friday June 2nd. Together with Giuseppe Silano, Chiara Gabellieri and Wolfgang Hönig, we will be organizing a half day workshop focused on UAV-specific simulation in robotics. We have invited some great speakers namely: Tomáš Báča, Davide Scaramuzza, Angela Schoellig and Jaeyoung Lim. The topics will cover multi-YAV simulation to realistic vision-based rending and software-in-the-loop handling for PX4.

Additionally, participants can submit an extended abstract to be invited for an poster presentation during the same workshop. The submission deadline has been extended to April 3rd, so for more information about submission, schedule and speaker info, go to the workshop’s website.

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

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

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

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

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

And, without further ado, here it is:

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

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

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

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

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

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

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

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

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

Hanna

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

Announcement: We will have a townhall meeting this Wednesday (7th of December) about Crazyradio 2.0 and the ideas about the new com-stack at 15:00 (3 pm) CET. Please follow the discussion here for more info.

As you have been very much aware of already if you have been reading the blog occasionally is that we went to Japan with the entire company to be at the International Conference on Intelligent Robots and Systems (IROS) in Kyoto, Japan. Besides eating great food, singing karaoke, and herding our fully onboard autonomous swarm at our stand, we also had some time to check out what kind of work was done with the Crazyflie in the proceeding papers and talks!

So just some generic statistics first:

  • IROS had 1716 papers accepted
  • We found 14 Crazyflie papers/posters and 2 workshop papers
  • The three biggest topics we found the papers in were: SLAM, Multi-robot systems and Navigation & Motion planning, SLAM

At ICRA this year, we noticed that the Crazyflie/bolt were used to make unconventional platforms, like a mono-copter or transforming the Crazyflie to a Pogo stick. It was interesting to see that now at IROS, the focus seemed to be more on navigation, localization and even SLAM… also with unconventional sensors!

Navigation and SLAM with the Crazyflie

In the summer I (Kim) worked on a summer project with using ROS2 to try SLAM with the standard packages with the Flow deck and Multi-ranger. This was also to present the work at ROScon before that with the Crazyswarm2 project, the Crazyflie can be used as an actual robotic platform too! I’m glad that some researchers already figured this one out already, as there were quite some papers on SLAM! [6] and [12] made use of the flow & multi-ranger but made their own custom algorithms to do SLAM and mapping that was more tailored to the task than the standard SLAM packages out there meant for 360 degree lidars.

Very interestingly, there were several papers that uses unconventional sensors for this as well. [5] used a gas sensor to do both gas source localization and distributing mapping and [10] made their own echolocation deck with buzzer + microphones. Let’s see what other sensors will be explored in the future!

Safe Robot Learning Competition

A special mention goes to the Safe Robot Learning competition, organized by the joined TU Munich and Utoronto’s the Learning system & robotics lab (formally known as the Dynamic Systems lab). In this competition, teams could participate with an online competition where they had to finish an obstacle course in simulation. From those that were successful, the finals were done with a real Crazyflie at a remote testbed in the University of Toronto, where the algorithms were put to the ultimate test! The simulation was done in the safe-control-gym framework [12], and the communication with the real Crazyflie was done with the ROS1 based Crazyswarm. We sponsored the first three places with a couple of Crazyflie bundles, so congrats to the winners!

List of IROS 2022 Papers featuring the Crazyflie

  1. Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors Sven Gronauer, Matthias Kissel, Luca Sacchetto, Mathias Korte and Klaus Diepold
  2. Downwash-aware Control Allocation for Over-actuated UAV Platforms Yao Su , Chi Chu , Meng Wang , Jiarui Li , Liu Yang , Yixin Zhu , Hangxin Liu
    • Beijing Institute for General Artificial Intelligence (BIGAI)
    • ArXiv
    • IEEE Xplore
  3. Towards Specialized Hardware for Learning-based Visual Odometry on the Edge Siyuan Chen and Ken Mai
    • Beijing Institute for General Artificial Intelligence (BIGAI)
    • IEEE Xplore
  4. Polynomial Time Near-Time-Optimal Multi-Robot Path Planning in Three Dimensions with Applications to Large-Scale UAV Coordination Teng Guo, Siwei Feng and Jingjin Yu
  5. GaSLAM: An Algorithm for Simultaneous Gas Source Localization and Gas Distribution Mapping in 3D Chiara Ercolani, Lixuan Tang and Alcherio Martinoli
    • Ecole Polytechnique Federale de Lausanne (EPFL),
    • IEEE Xplore
  6. Efficient 2D Graph SLAM for Sparse Sensing Hanzhi Zhou, Zichao Hu, Sihang Liu and Samira Khan
  7. Avoiding Dynamic Obstacles with Real-time Motion Planning using Quadratic Programming for Varied Locomotion Modes Jason White, David Jay, Tianze Wang, and Christian Hubicki
  8. Dynamic Compressed Sensing of Unsteady Flows with a Mobile Robot Sachin Shriwastav, Gregory Snyder and Zhuoyuan Song
  9. A Framework for Optimized Topology Design and Leader Selection in Affine Formation Control Fan Xiao, Qingkai Yang, Xinyue Zhao and Hao Fang
  10. Blind as a bat: audible echolocation on small robots Frederike Dumbgen Adrien Hoffet Mihailo Kolundzija Adam Scholefield Martin Vetterli
    • Ecole Polytechnique Federale de Lausanne (EPFL)
    • IEEE xplore
  11. Safe Reinforcement Learning for Robot Control using Control Lyapunov Barrier Functions Desong Du, Shaohang Han, Naiming Qi and Wei Pan
    • Harbin Institute of Technology + TU Delft + University of Manchester
    • Late breaking result poster
  12. Parsing Indoor Manhattan Scenes Using Four-Point LiDAR on a Micro UAV Eunju Jeong, Suyoung Kang, Daekyeong Lee, and Pyojin Kim
    • Sookmyung Women’s University,
    • Late breaking result poster
  13. Interactive Multi-Robot Aerial Cinematography Through Hemispherical Manifold Coverage Xiaotian Xu , Guangyao Shi , Pratap Tokekar , and Yancy Diaz-Mercado
    • University of Maryland
    • Note: Only mention of Crazyflie experiments in presentation
  14. Safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics Zhaocong Yuan, Adam W. Hall, Siqi Zhou, Lukas Brunke, Melissa Greeff, Jacopo Panerati, Angela P. Schoellig
  15. Distributed Geometric and Optimization-based Control of Multiple Quadrotors for Cable-Suspended Payload Transport Khaled Wahba and Wolfgang Hoenig
  16. Customizable-ModQuad: a Versatile Hardware-Software Platform to Develop Lightweight and Low-cost Aerial Vehicles Diego S. D’Antonio, Jiawei Xu, Gustavo A. Cardona, and David Saldaña

Let us know if we are missing any papers or information per papers! Once the IEEE xplore IROS 2022 proceedings have been published, we will update these too and put them on our research page.

This week’s guest blogpost is from Rik Bouwmeester from the Micro Air Vehicle lab, Faculty of Aerospace Engineering at the Delft University of Technology.

Tiny quadcopters like the Crazyflie can be operated in narrow, cluttered environments and in proximity to humans, making them the perfect candidate for search-and-rescue operations, monitoring of crop in a greenhouse, or performing inspections where other flying robots cannot reach. All these applications benefit from autonomy, allowing deployment without proximity to a base station or human operator and permitting swarming behavior.

Achieving autonomous navigation on nano quadcopters is challenging given the highly constrained payload and computational power of the platform. Most attention has been given to monocular solutions; the camera is a lightweight and energy-efficient passive sensor that captures rich information of the environment. One of the most important monocular visual cues is optical flow, which has been exploited on MAVs with higher payload for obstacle avoidance [1], depth estimation [2] and several bio-inspired methods for autonomous navigation [3–7].

Optical flow describes the apparent visual variations caused by relative motion between an observer and their surroundings. This rich visual cue contains tangled information of velocity and depth. However, calculating optical flow is expensive. The field of optical flow estimation is and has been for a couple of years dominated by convolutional neutral networks (CNNs). Despite efforts to find architectures of reduced size and latency [8-10], these methods are still highly computationally expensive, running at several to tens of FPS on modern desktop GPUs and requiring millions of parameters to run, rendering them incompatible with edge hardware.

To this end, we present “NanoFlowNet: Real-Time Dense Optical Flow on a Nano Quadcopter”, submitted to an international robotics conference, which introduces NanoFlowNet, a CNN architecture designed for real-time, fully on-board, dense optical flow estimation on the AI-deck.

CNN architecture

We adopt semantic segmentation CNN STDC-Seg [11] and modify it for optical flow estimation. The resulting CNN architecture may be considered “real-time” on desktop hardware, for deployment on edge devices such as a nano quadcopter the net must be significantly shrunk. We improve the latency of the architecture in three ways.

First, we redesign the key convolutional modules of the architecture, the Short-Term Dense Concatenate (STDC) module. By reordering the operations within the strided variant of the module, we save, depending on the location of the module within the architecture, from over 10% to over 50% of the MAC operations per module, while increasing the number of output filters with large receptive field size. A large receptive field size is desirable for optical flow estimation.

Second, inspired by MobileNets [12], we globally replace ‘regular’ convolutions with depthwise separable convolutions. Depthwise separable convolutions factorize a convolution into a depthwise and pointwise convolution, effectively reducing the calculational expense at a cost in representational capacity.

Third, we reduce the input dimensionality. We train and infer network on grayscale input images, reducing the required on-board memory for storing images by a factor 2/3. Any memory saved on the AI-deck’s L2 memory can be handed to AutoTiler for storing the CNN architecture, speeding up the on-board execution. Requiring more of a speed-up, we run the CNN on-board at a reduced input resolution of 160×112 pixels. Besides the speed-up through saved L2, reducing the input resolution makes all operations throughout the network cheaper. We downscale training data to closely match the target resolution. Both these changes come at a loss of input information. We will miss out on small objects and small displacements that are not captured by the resolution.

To give some intuition of the available memory: Estimating optical flow requires two input images. Storing two color input images at full resolution requires (2 x 324x324x3=) 630 kB. The AI-deck has 512 kB of L2 memory available.

Motion boundary detail guidance

Inspired by STDC-Seg, we guide the training of optical flow with a train-time-only auxiliary task to promote the encoding of spatial information in the early layers. Specifically, we introduce a motion boundary prediction task to the net. The motion boundary ground truth can be found in the optical flow datasets. This improves performance by 0.5 EPE on the MPI Sintel clean (train) benchmark, at zero cost to inference latency.

Performance on MPI Sintel

Given the scaling and conversion to grayscale of input data, our network is not directly comparable with results reported by other works. For comparison, we retrain one of the fastest networks in literature, Flownet2-s [13], on the same data. Given the reduction in resolution, we drop the deepest two layers to maintain a reasonable feature size. We name the model Flownet2-xs.

We benchmark the performance of the architecture on the optical flow dataset MPI Sintel. NanoFlowNet performs better than FlowNet2-xs, despite using less than 10% of the parameters. NanoFlowNet achieves 5.57 FPS on the AI-deck. FlowNet2-xs does not fit on the AI-deck due to the network size. To put the achieved latency of NanoFlowNet in perspective, we execute FlowNet2-xs’ first two convolutions and the final prediction layer on the GAP8. The three-layer architecture achieves 4.96 FPS, which is slower than running the entire NanoFlowNet. On a laptop GPU, the two architectures accomplish similar latency.

MethodMPI Sintel (train) [EPE]Frame rate [FPS]Parameters
CleanFinalGPUGAP8
FlowNet2-xs9.0549.4581501,978,250
NanoFlowNet7.1227.9791415.57170,881
Performance on MPI Sintel (train subset). (Average) end-to-end Point Error (EPE) describes how far off the estimated flow vectors are on average, lower is better.

Obstacle avoidance implementation

We demonstrate the effectiveness of NanoFlowNet by implementing it in a simple, proof-of-concept obstacle avoidance application on an AI-deck equipped Crazyflie. We let the quadcopter fly forward at constant velocity and implement the horizontal balance strategy [14], [15], where the quadcopter balances the optical flow in the left and right half plane by yawing.

We equip a Crazyflie with the Flow deck for positioning only. The total flight platform weighs 34 grams.

We augment the balance strategy by implementing active oscillations (a cyclic up-down movement), resulting in additional optical flow generated across the field of view. This is particularly helpful for avoiding obstacles in the direction of horizontal travel, since no optical flow is generated at the focus of expansion.

The obstacle avoidance implementation is demonstrated in an open and a cluttered environment in ‘the Cyber Zoo’, an indoor flight arena at the faculty of Aerospace Engineering at the Delft University of Technology. The control algorithm is most robust in the open environment, with the quadcopter managing to drain a full battery without crashing. In the cluttered environment, performance is more variable. Especially on occasions where obstacles are close to one another, the quadcopter tends to avoid the first obstacle successfully, only to turn straight into the second and crash into it. Adding a head-on collision detection based on FOE detection and divergence estimation (e.g., [7]) should help avoid obstacles in these cases.

Successful run in a cluttered environment in the Cyber Zoo. The Crazyflie manages to avoid collision until the battery is drained.

All in all, we consider the result a successful demonstration of the optical flow CNN. In future work, we expect to see applications that take more advantage of the resolution of the flow information.

Citation

Bouwmeester, R. J., Paredes-Vallés, F., De Croon, G. C. H. E. (2022). NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter. arXiv. https://doi.org/10.48550/arXiv.2209.06918

References

[1] Gao, P., Zhang, D., Fang, Q., & Jin, S. (2017). Obstacle avoidance for micro quadrotor based on optical flow. Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017, 4033–4037. https://doi.org/10.1109/CCDC.2017.7979206

[2] Sanket, N. J., Singh, C. D., Ganguly, K., Fermuller, C., & Aloimonos, Y. (2018). GapFlyt: Active vision based minimalist structure-less gap detection for quadrotor flight. IEEE Robotics and Automation Letters, 3(4), 2799–2806. https://doi.org/10.1109/LRA.2018.2843445

[3] Conroy, J., Gremillion, G., Ranganathan, B., & Humbert, J. S. (2009). Implementation of wide-field integration of optic flow for autonomous quadrotor navigation. Autonomous Robots, 27(3), 189–198. https://doi.org/10.1007/s10514-009-9140-0

[4] Zingg, S., Scaramuzza, D., Weiss, S., & Siegwart, R. (2010). MAV navigation through indoor corridors using optical flow. Proceedings – IEEE International Conference on Robotics and Automation, 3361–3368. https://doi.org/10.1109/ROBOT.2010.5509777

[5] De Croon, G. C. H. E. (2016). Monocular distance estimation with optical flow maneuvers and efference copies: A stability-based strategy. Bioinspiration and Biomimetics, 11(1). https://doi.org/10.1088/1748-3190/11/1/016004

[6] Serres, J. R., & Ruffier, F. (2017). Optic flow-based collision-free strategies: From insects to robots. Arthropod Structure and Development, 46(5), 703–717. https://doi.org/10.1016/j.asd.2017.06.003

[7] De Croon, G. C. H. E., De Wagter, C., & Seidl, T. (2021). Enhancing optical-flow-based control by learning visual appearance cues for flying robots. Nature Machine Intelligence, 3(1), 33–41. https://doi.org/10.1038/s42256-020-00279-7

[8] Ranjan, A., & Black, M. J. (2017). Optical flow estimation using a spatial pyramid network. Proceedings – 30th IEEE Conference on Computer Vision and Pattern Recognition, 2720–2729. https://doi.org/10.1109/CVPR.2017.291

[9] Hui, T. W., Tang, X., & Loy, C. C. (2018). LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8981–8989. https://doi.org/10.1109/CVPR.2018.00936

[10] Sun, D., Yang, X., Liu, M. Y., & Kautz, J. (2017). PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8934–8943. https://doi.org/10.1109/CVPR.2018.00931

[11] Fan, M., Lai, S., Huang, J., Wei, X., Chai, Z., Luo, J., & Wei, X. (2021). Rethinking BiSeNet For Real-time Semantic Segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 9711–9720. https://doi.org/10.1109/CVPR46437.2021.00959

[12] Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. In arXiv. arXiv. http://arxiv.org/abs/1704.04861

[13] Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., & Brox, T. (2017). FlowNet 2.0: Evolution of optical flow estimation with deep networks. Proceedings – 30th IEEE Conference on Computer Vision and Pattern Recognition, 1647–1655. https://doi.org/10.1109/CVPR.2017.179

[14] Souhila, K., & Karim, A. (2007). Optical flow based robot obstacle avoidance. International Journal of Advanced Robotic Systems, 4(1), 2. https://doi.org/10.5772/5715

[15] Cho, G., Kim, J., & Oh, H. (2019). Vision-based obstacle avoidance strategies for MAVs using optical flows in 3-D textured environments. Sensors, 19(11), 2523. https://doi.org/10.3390/s19112523

This weeks guest blog post is from Hanna Müller, Vlad Niculescu and Tommaso Polonelli, who are working with Luca Benini at the Integrated Systems Lab and Michele Magno at the Center for Project-Based Learning, both at ETH Zürich. Enjoy!

This blog post will give you some insight into our current work towards autonomous flight on nano-drones using a miniaturized multi-zone depth sensor. Here we will mainly talk about obstacle avoidance, as it is our first building block towards fully autonomous navigation. Who knows, maybe in the future, we will have the honor to write another blog post about localization and mapping ;)

A Crazyflie 2.1 with our custom multi-zone ToF deck, a flow deck and a vicon marker.

Obstacle avoidance on nano-drones is challenging, as the restricted payload limits on-board sensors and computational power. Most approaches, therefore, use lightweight and ultra-low-power monocular cameras (as the AI-deck) or 1d depth sensors (as the multi-ranger deck). However, both those approaches have drawbacks – the camera images need extensive processing, usually even neural networks to detect obstacles. Neural networks additionally need training data and are prone to fail in completely new scenarios. The 1d depth sensors can reliably detect obstacles in their field of view (FoV); however, no information about the size or exact position of the obstacle is obtained.


On bigger drones, usually lidars or radars are used, but unfortunately, due to the limited weight and power consumption, those cannot be carried and used on nano-drones. However, in 2021 STMicroelectronics introduced a new multi-zone Time-of-Flight (ToF) sensor – with maximal 8×8 pixel resolution, a range up to 4m (according to the datasheet), a small form-factor and low power consumption of only 286mW (typical) it is ideal to use on nano-drones.


In the picture on top, you can see the Crazyflie 2.1 with our custom ToF deck (open-sourced at https://github.com/ETH-PBL/Matrix_ToF_Drones). We described this deck for the first time in [1], together with a sensor characterization. From this, we saw that we could use the sensor in different light conditions and on different colored obstacles, but from 2m on, the measurements started to get incomplete in all scenarios. However, as the sensor can detect invalid measurements (due to interference or obstacles being out of range), we can still rely on our information. In [2], we presented the system and some steps towards obstacle avoidance in a demo abstract, as you can see in the video below:

The next thing we did was to collect a dataset – we flew with different combinations of decks (flow-deck v2, AI-deck, our custom multi-zone ToF deck) and sometimes even tracked by a vicon system. Those recordings amount to an extensive dataset with depth images, RGB images, internal state estimation and the position and attitude ground truth.


We then fed the recorded data into a python simulation to develop an obstacle avoidance algorithm. We focused on only the ToF data (we are not fusing with the camera in this project, we just provide the data for future work). We aimed for a very efficient solution – because we want it to run on-board, on the STM32F405, with low latency and without occupying too many resources. Our algorithm is very lightweight but highly effective – we divide the FoV in different zones, according to how dangerous obstacles in those areas are and then use a decision tree to decide on a steering angle and velocity.


With only using up 0.31% of the computational power and 210 μs latency, we reached our goal of developing an efficient obstacle avoidance algorithm. Our system is also low-power, the power to lift the additional sensor with all accompanying electronics as well as the supply of it totals in less than 10% of the whole drone. On average, our system reaches a flight time of around 7 minutes. We refer to our preprint [3] for details on our various tests – they include flights with distances up to 212 m and 100% reliability and high agility at a low speed in an office environment.

As our paper is currently submitted but not yet accepted our code and dataset are not yet released – however, the hardware design is already accessible: https://github.com/ETH-PBL/Matrix_ToF_Drones

[1] V. Niculescu, H. Müller, I. Ostovar, T. Polonelli, M. Magno and L. Benini, “Towards a Multi-Pixel Time-of-Flight Indoor Navigation System for Nano-Drone Applications,” 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2022, pp. 1-6, doi: 10.1109/I2MTC48687.2022.9806701.
[2] I. Ostovar, V. Niculescu, H. Müller, T. Polonelli, M. Magno and L. Benini, “Demo Abstract: Towards Reliable Obstacle Avoidance for Nano-UAVs,” 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2022, pp. 501-502, doi: 10.1109/IPSN54338.2022.00051.
[3] H.Müller, V. Niculescu, T. Polonelli, M. Magno and L. Benini “Robust and Efficient Depth-based Obstacle Avoidance for Autonomous Miniaturized UAVs”, submitted to IEEE, preprint: https://arxiv.org/abs/2208.12624

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

Unmanned Aerial Vehicles (UAVs) have garnered much attention from both researchers and engineers in recent decades. Aerial robots in general are classified into mainly three categories: fixed wings, rotary wings and flapping wings.

Fixed wings are one of the most common aerial vehicles as it has relatively higher power efficiency and payload capacity than other types, thanks to their big and highly customizable wing. But this also leads to a bigger footprint and usually the lack of ability for Vertical Taking Off and Landing (VTOL). Rotary wings generally include helicopter and multirotors (such as quadrotors), and they have recently become increasingly popular in our daily lives. Easily achieving great performance in attitude and position control, rotary wings are widely applied in many fields. Flapping wing robots take inspirations from small flapping insects (such as Harvard Robobee) or birds (Purdue Hummingbird Robot).

Fig: A simple prototype of SAM from SUTD with Crazyflie Bolt.

Monocopters are largely inspired from the falling motion of maple seeds, and they are relatively much simpler to build as compared to its counterparts. They can keep a relative smaller footprint and achieve decent control performance although they are highly underactuated. The Single Actuator Monocopter (SAM) has the ability to VTOL, perform 3D trajectory tracking as well as maintain high hovering efficiency. With those advantages, rapid developments have been made in recent years such as the Foldable Single Actuator Monocopter (F-SAM) and Modular Single Actuator Monocopter (M-SAM) from Engineering Product Development (EPD) of Singapore University of Technology and Design (SUTD).

Taking inspiration from nature – Samara inspired monocopter

A descending samara or maple seed, is able to passively enter auto-rotation motion and stabilize its flight attitude, helping to slow down its descent speed and travel further for better survival of the species. This natural behavior attracts interests from scientists and researchers. With previous studies, we learnt that this passive attitude stability is mainly guaranteed by mass distribution (Center of Mass) and wing geometry (Center of Pressure) as well as the rotation motion.

A maple seed inspired Single Actuator Monocopter (SAM).

The SAM is designed to be very close in its mechanical make-up to its natural sibling, having a large single wing structure and a smaller, denser ‘seed’ structure. A single motor with propeller is installed on the leading edge, parallel to the wing surface. Comparing with flight dynamics of the original maple seed, SAM has extra torques and force caused by the spinning propeller, including a reaction torque and thrust directly from propeller, as well as an extra torque caused by precession motion. As a result, the balance of the combined forces and torques allows SAM to enter a new equilibrium condition while still retaining the passive attitude stability.

Development of monocopters

The research on monocopters can be traced back to a long time ago. Here are some examples of different types of air frame to roughly introduce their developments. An air-frame called Robotic Samara [1] was created in 2010, which has a motor to provide rotational force, a servo to control collective pitch of the wing, a winged body fabricated by carbon fiber, and a lipo battery. In the following year, Samarai MAV [2] was developed by following the mass distribution of a natural maple seed. To achieve the control, a servo is equipped to regulate the wing flap. In 2020, a single actuator monocopter was introduced with a simplified air-frame [3]. The main structure is made by laminated balsa wood while the trailing edge of the wing is made by foam for better mass distribution. By making use of the passive attitude stability, only one actuator is required to control the position in 3D space. Based on which, F-SAM [4] and M-SAM [5] were developed in 2021 and 2022 respectively.

SAM with foldable wing structure (F-SAM).

A Modular SAM (M-SAM) with Crazyflie Bolt

Thanks to its easy implementation and reliable performance, we use the Crazyflie Bolt as the flight controller for M-SAM. Like other robotic systems, the ground station is integrated with motion capture system (position and attitude feedback for both control and ground truth) and a joystick (control reference directly generated by user) is responsible for sending filtered state feedbacks and control references or control signal directly to flight controller. This is realized by employing the Crazyradio PA under the Crazyflie-lib-python environment. Simple modifications from the original firmware were made to map from the control reference to motor command (a customized flight controller).

A diagram shows how Crazyflie Bolts work in M-SAM project.

Another advantage of using Crazyflie Bolt in M-SAM project is its open source swarm library. Under the swarm environment, SAMs can fly in both singular and cooperative configurations. With simple human assistance, two SAMs can be assembled into cooperative configuration by making use of a pair of magnetic connectors. The mid-air separation from cooperative configuration to singular configuration is passively triggered by increasing the rotating speed until the centrifugal force overcomes the magnetic force.

Modular Single Actuator Monocopters (M-SAM), which is able to fly in both singular and cooperative configuration.

Potential applications

What kinds of applications can be achieved with the monocopter aerial robotic platform? On the one hand, many applications are limited by the nature of self-rotation motion. On the other hand, the passive rotating body also offers advantages in some special scenarios. For example, SAM is an ideal platform for LIDAR application, which usually requires the rotating motion to sense the environment around. Besides, thanks to simple mechanical design and cheap manufacturing cost, SAM can be designed for one time use such as light weight air deployment or unknown, dangerous environments.

An example [6] shows the potential applications of a rotating robot with camera.

Reference

  • [1] Ulrich, Evan R., Darryll J. Pines, and J. Sean Humbert. “From falling to flying: the path to powered flight of a robotic samara nano air vehicle.” Bioinspiration & biomimetics 5, no. 4 (2010): 045009.
  • [2] Fregene, Kingsley, David Sharp, Cortney Bolden, Jennifer King, Craig Stoneking, and Steve Jameson. “Autonomous guidance and control of a biomimetic single-wing MAV.” In AUVSI Unmanned Systems Conference, pp. 1-12. Arlington, VA: Assoc. for Unmanned Vehicle Systems International, 2011.
  • [3] Win, Luke Soe Thura, Shane Kyi Hla Win, Danial Sufiyan, Gim Song Soh, and Shaohui Foong. “Achieving efficient controlled flight with a single actuator.” In 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 1625-1631. IEEE, 2020.
  • [4] Win, Shane Kyi Hla, Luke Soe Thura Win, Danial Sufiyan, and Shaohui Foong. “Design and control of the first foldable single-actuator rotary wing micro aerial vehicle.” Bioinspiration & Biomimetics 16, no. 6 (2021): 066019.
  • [5] X. Cai, S. K. H. Win, L. S. T. Win, D. Sufiyan and S. Foong, “Cooperative Modular Single Actuator Monocopters Capable of Controlled Passive Separation,” 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 1989-1995, doi: 10.1109/ICRA46639.2022.9812182.
  • [6] Bai, Songnan, Qingning He, and Pakpong Chirarattananon. “A bioinspired revolving-wing drone with passive attitude stability and efficient hovering flight.” Science Robotics 7, no. 66 (2022): eabg5913.

This week we have a guest blog post from Jiawei Xu and David Saldaña from the Swarmslab at Lehigh University. Enjoy!

Limits of flying vehicles

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 [1][2], 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.

Fig 1. A crazyflie has four propellers generating thrust forces in parallel. Credit to: https://robots.ros.org/crazyflie/

Improving strength

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 [3], 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.

Improving controllability

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 Design

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 [4][5], we introduced two types of modules which are R-modules and T-modules.

Fig 2. Major components of an H-ModQuad “T-module” we are using in our project. We use Bitcraze Crazyflie Bolt as the central control board.

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.

Structure 1: Composed of four types of R-modules.

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.

Videos

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.

Reference

[1] 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.

[2] 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.

[3] 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.

[4] 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.

[5] 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.