It’s time for an update on the Multi-ranger deck (see previous updates here: 1, 2, 3). Back in February/March we were just about to start the production of the Multi-ranger deck when the new VL51L1 ToF sensor from ST became available. Among the interesting features for the new sensor is increased range and ROI (region of interest) settings. We felt that the improvement was enough to consider using the new sensor for the Multi-ranger so we got some sensors and started testing.
Point cloud
We’ve made a little example where you can control the Crazyflie with a keyboard (using velocity mode) that records estimated position, body attitude and all the distances (down/up, left/right, front/back) from the ToF sensors. We then did some post-processing of the log data and plotted it using pyntcloud, you can see the results in the point cloud. There are still lots of possible improvements (like taking body attitude into consideration) to be made on the script, but once we’ve cleaned it up a bit we’ll publish it on GitHub so others can play around with it. Note that in the plot the blue points are up/down sensors (i.e Crazyflie movement) and the red points are the side sensors (front/back/left/right).
The room that was mapped
So far we’re happy with the results. We feel that the increased range and new features enables users to work on more interesting problems with the deck, so we’ve decided to switch our the sensors and go to production with the new one. Right now we’re running a 0-series of 10 units using the real production manufacturing fixture (for the standing PCBs with sensors) as well as the production test rig. Our best estimate for when the deck will become available for purchase is some time during the summer. Below is a picture of the latest prototype. We’ll make sure to keep you updated on the progress!
Warehouse issues
On an additional note we’re having some issues with our warehouse provider which ships out orders from our E-store. In the beginning of the month the provider hade scheduled a move of the warehouse to a new physical location which would delay handling of orders for max a week. Unfortunately the move, which should have been finished mid last week, is still in progress which means we can’t ship out any orders for the moment. We’re working hard on trying to work this out with the provider.
I’ve spent the last 5 years of my career at Microsoft on the team responsible for HoloLens and Windows Mixed Reality VR headsets. Typically, augmented reality applications deal with creating and manipulating digital content in the context of real-world surroundings. I thought it’d be interesting to explore some applications of using an augmented reality device to manipulate and control physical objects and have them interact with the real world and/or digital content.
Phase 1: Gesture Input
The HoloLens SDK has APIs for consuming hand gestures as input. For the first phase of this project, I modified the existing Windows UAP/UWP client to handle these gestures and convert them to CRTP setpoints. I used the “manipulation gesture” which provides offsets in three dimensions for a tap-and-drag gesture, from the point in space where the initial tap occurred. These three degrees of freedom are mapped to thrust, pitch and roll.
For the curious, there’s an article on my website with details about the implementation and source code. Here’s a YouTube video where I explain the concept and show a couple of quick demos.
As you can see in the first demo in the video, this works but isn’t entirely useful or practical. The HoloLens accounts for head movements (otherwise moving the head to the left would produce the same offset as moving the hand to the right, requiring the user to keep his or her head very still) but the user must still take care to keep the hand in the field of view of the device’s cameras. Once the gesture is released (or the hand goes out of view) the failsafe engages and the Crazyflie drops to the ground. And of course, lack of yaw control cripples the ability to control the Crazyflie.
Phase 2: Position Hold
Adding a flow deck makes for a more compelling user experience, as seen in the second demo in the video above. The Crazyflie uses the sensors on the flow deck to hold its position. With this functionality, the user is free to move about the room and make shorter “adjustment” hand gestures, instead of needing to hold very still. In this mode, the gesture’s degrees of freedom map to an x/y velocity and a vertical offset from the current z-depth.
This is a step in the right direction, but still has limitations. The HoloLens doesn’t know where it is in space relative to the Crazyflie. A gesture in the y axis relative to the device will always result in a movement in the y direction of the Crazyflie, which begins to feel unnatural if the user moves around. Ideally, gestures would cause the crazyflie to move in the same direction relative to the user, not relative to the ‘front’ of the Crazyflie. Also, there’s still no control over yaw.
The flow deck has some limitation as well: The z-range only goes to 2 meters with any accuracy. The flow sensor (for lateral stabilization) has a strong dependency on the patterns on the floor below. A flow sensor is a camera that relies on measuring pixel deltas from frame to frame, so if the floor is blank or has a repeating pattern, it can be difficult to hold position properly.
Despite these limitations, using hand gestures to control the Crazyflie with a flow deck installed as actually quite fun and surprisingly easy.
Phase 3 and Beyond: Future Work & Ideas
I’m currently working on some new features that I hope will open the door for more interesting applications. All of what follows is a work in progress, and not yet implemented or functional. Dream with me!
Shared Coordinate System
The next phase (currently a work in progress) is to get the HoloLens and the Crazyflie into a shared coordinate system. Having spatial awareness between the HoloLens and the Crazyflie opens up some very exciting scenarios:
The orientation problem could be improved: transforms could be applied to gestures to cause the Crazyflie to respond to commands in the user’s frame of reference (so ‘pushing’ away from one’s self would cause the Crazyflie to fly away from the user, instead of whatever direction is ‘forward’ to the Crazyflie’s perspective).
A ‘follow me’ mode, where the crazyflie autonomously follows behind a user as he or she moves throughout the space.
Ability to walk around and manually set waypoints by selecting points of interest in the environment.
The Loco Positioning System is a natural fit here. A setup step (where a spatial anchor or similar is established at same physical position and orientation as the LPS origin) and a simple transform for scale and orientation (HoloLens and the Crazyflie define X,Y,Z differently) would allow the HoloLens and Crazyflie to operate in a shared coordinate system. One could also use the webcam on the HoloLens along with computer vision techniques to track the Crazyflie, but that would require constant line of sight from the HoloLens to the Crazyflie.
Obstacle Detection/Avoidance
The next step after establishing a shared coordinate system is to use the HoloLens for obstacle detection and avoidance. The HoloLens has the ability to map surfaces in real time and position itself in that map (SLAM). Logic could be added to the HoloLens to consume this surface map and adjust pathing/setpoints to avoid these obstacles without reducing the overall compute/power budget of the Crazyflie itself.
Swarm Control and Manipulation
As a simple extension of the shared coordinate system (and what Bitcraze has been doing with TDoA and swarming lately) the HoloLens could be used to manipulate individual Crazyflies within a swarm through raycasting (the same technique used to gaze at, select and move specific holograms in the digital domain). Or perhaps a swarm could be controlled to move out of the way as a user passes through the swarm, and return to formation afterward.
Augmenting with Digital Content
All scenarios discussed thus far have dealt with using the HoloLens as an input and localization device, but its primary job is to project digital content into the real world. I can think of applications such as:
Games
Flying around through a digital obstacle course
First person shooter or space invaders type game (Crazyflie moves around to avoid user or fire rendered laser pulses at user, etc)
Diagnostic/development tools
Overlaying some diagnostic information (such as battery life) above the Crazyflie (or each Crazyflie in a swarm)
Set or visualize/verify the position of the LPS nodes in space
Visualize the position of the Crazyflie as reported by LPS, to observe error or drift in real time
Conclusion
There’s no shortage of interesting applications related to blending augmented reality with the Crazyflie, but there’s quite a bit of work ahead to get there. Keep an eye on the Bitcraze blog or the forums for updates and news on this effort.
I’d love to hear what ideas you have for combining augmented reality devices with physical devices like the Crazyflie. Leave a comment with thoughts, suggestions, or any other relevant work!
Here at the USC ACT Lab we conduct research on coordinated multi-robot systems. One topic we are particularly interested in is coordinating teams consisting of multiple types of robots with different physical capabilities.
A team of three quadrotors all controlled with Crazyflie 2.0 and a Clearpath Turtlebot
Applications such as search and rescue or mapping could benefit from such heterogeneous teams because they allow for more flexibility in the choice of sensors and locomotive capability. A core challenge for any multi-robot application is motion planning – all of the robots in the team need to make it to their target locations efficiently while avoiding collisions with each other and the environment. We have recently demonstrated a scalable method for trajectory planning for heterogeneous robot teams utilizing the Crazyflie 2.0 as the flight controller for our aerial robots.
A Crazier Swarm
To test our trajectory planning research we wanted to assemble a team with both ground robots and multiple sizes of aerial robots. We additionally wanted to leverage our existing Crazyswarm software and experience with Crazyflie firmware to avoid some of the challenges of working with new hardware. Luckily for us the BigQuad deck offered a straightforward way to super-size the Crazyflie 2.0 and gave us the utility we needed.
With the BigQuad deck and off-the-shelf components from the hobbyist drone community we built three super-sized Crazyflie 2.0s. Two of them weigh 120g (incl. battery) with a motor-to-motor size of 130mm, and the other is 490g (incl. battery and camera) with a size of 210mm.
120g, 130mm
490g, 210mm
We wanted to pick components that would be resistant to crashing while still offering high performance. To meet these requirements we ended up picking components inspired by the FPV drone racing community where both reliable performance and high-impact crashes are expected. Full parts lists for both platforms are available here
Integrating the new platforms into the Crazyswarm was fairly easy. We first had to re-tune the PID controller gains to account for the different dynamics of the larger platforms. This didn’t take too long, but we did crash a few times — luckily the components we chose were able to handle the crashes without any breakages. After tuning the platforms behave very well and are just as easy to work with as the original Crazyflie 2.0. We additionally updated the Crazyswarm package to be able to differentiate between BigQuad and regular Crazyflie types and those updates are now available for use by anyone!
In future work, we are excited to do hands-on experiments with a prototype of the CF-RZR. This new board seems like a promising upgrade to the CF 2.0 + BQD combination as it has upgraded components, an external antenna, and a standardized form factor. Hopefully we will see the CF-RZR as part of the Crazyswarm in the near future!
We have been flying swarms in our office plenty of times. There is kind of a limitation to this though, our flying space is only around 4 x 4 meters. Flying 8 – 10 Crazyflies in this space is challenging and it is hard do make it look good. Slight position inaccuracy makes it look a bit sloppy. To mitigate this we decided to have a small swarm show using a a bigger flying space and to invite families and friends, just to raise the stake a bit.
As usual we had limited time to accomplish this, and this time the result should be worth looking at. Well, we have managed to pull off hard things in one day before so why not this time… The setup is basically a swarm bundle with added LED-rings. Kristoffer took care of the choreography, Tobias setting up the drones and Arnaud configuring the Loco positioning system.
Choreography
Kristoffers pre-Bitcraze history involves some dancing and he has been playing a bit earlier with the idea of creating choreographies with Crazyflies. One part of this was a weekend-hack a few months back when he tried to write a swarm sequencer that is a bit more dance oriented. The goal was to be able to run a sequence synchronized to music and define the movements in terms of bars and beats rather than seconds. He also wanted to be able to define a motion to end at a specific position at a beat as opposed to start on the beat. As Kristoffer did not have access to a swarm when he wrote the code he also added a simple simulator to visualize the swarm. The hack was not a complete success at that time but turned out to be useful in this case.
The sequences are defined in a YML file as a list of time stamps, positions and, if needed the color of the LED-ring. After a few hours of work he had at least some sort of choreography with 9 Crazyflies moving around, maybe not a master piece from a dance point of view but time was running out.
The simulator is super basic but turned out to be very useful anyway (the color of the crosses indicates the color of the LED ring). We actually never flew the full sequence with all drones before the performance, but trusted the simulation to be accurate enough! We did fly most of the sequence with one Crazyflie, to at least make it plausible that we got it all right.
Short snippet from the simulation
Setting up drones
Handling swarms can be tedious and time consuming. Just making sure all drones are assembled, fully operational and charged is a challenge when the number increases. Tobias decided to do manual flight test of every drone. If it flies well manually it will most likely fly well autonomously. The testing resulted in switching out some motors and props as vibrations is a crippling factor, especially for Z accuracy. Takeaway from this exercise is to implement better self testing so this can be detected automatically and fixed much quicker.
Loco Positioning System
We ran the positioning system with standard firmware in TDoA mode to support multiple Crazyflies simultaneously. The mapped space was around 7 x 5 x 2.5 meters and the anchors were placed more or less in the corners of the flying space box.
The result
The audience (families and friends) was enthusiastic and expectations high! Even though not all drones made it all the way through the show, the spectators seemed to be duly impressed and requested a re-run.
After more than a year, Version 0.7.0 of the Android client has been released last Friday (March 16). Most importantly this release fixes a very annoying UI bug that appeared on newer Android versions, where the on-screen joystick did not show up when the app was started for the first time. It also adds support for height hold mode when using the zRanger or Flow deck, it adds a console view (can be enabled in Settings -> App settings -> Show console) and also shows the link quality for BLE connections now. You can read the full changelog on Github. You can find the release in the Google Play Store and as an APK on GitHub.
Connection quality and console messages now work on a BLE connection
Apart from the obvious/visible new features and bug fixes, quite a lot has happened “under the hood”. Some parts of the code were cleaned up, refactored, decoupled, etc. This is still a work in progress though.
There is still plenty of stuff to do for future releases, especially in the realm of Bluetooth support. On the short list are:
Param/Log support for BLE connections
bootloading over BLE
support for Flow deck sequences
Admittedly there was almost no documentation for the Android client and some features are hidden (too well). In an effort to change that, I’ve started to document some features on the project’s Github wiki.
If you find bugs in the app, want to request a feature or see errors in the documentation, please open a GitHub issue.
If you are interested in the development of the Crazyflie Android client and want to get involved, let me know. The fastest way to get new features added or bugs fixed is to contribute a pull request.
Last but not least, I’d like to thank the Bitcraze team for creating and developing the Crazyflie and amazing new decks. Maintaining the Crazyflie Android client is still one of my favorite past times. :)
We though we could use this Monday blog post to do a small state of the Crazyflie clients. What we call a Crazyflie client is a piece of software that connects a Crazyflie and allows to control it and get telemetry back from it. In this post we will concentrate on single-crazyflie client we have on our GitHub page, there exists a lot of libraries and software to control one or many Crazyflies, we will write another blog post about them.
Crazyflie PC client
The Crazyflie PC client, is what we consider the reference client. It supports connecting one Crazyflie using the Crazyradio (PA) dongle or direct USB connection to Crazyflie 2.0. It supports the full Crazyflie telemetry (ie. log), parameters (ie. params) and firmware update. It has support for all the Crazyflie 2.0 deck that can use client support. It is updated each time it is needed when new functionalities are added in the Crazyflie which makes it actively developed and maintained by the community and Bitcraze. A bluetooth link has not been prioritize so far since its multi-platform implementation is non-obvious and bluetooth will introduce some latency and lower the radio bandwidth compared to Crazyradio. However, if anyone would want bluetooth support for the Crazyflie PC client, we welcome contributions :-). The Crazyflie PC client is using the crazyflie-lib-python to communicate with the Crazyflie.
We have three mobile clients on our Github. They have various level of functionality depending on community involvement. Our philosophy is to have the mobile clients at least able to control a Crazyflie, this allows to use them to test Crazyflies without requiring to setup a computer. We will help and support anyone that is interested in adding functionalities to the mobile clients but we generally do not have time to add much functionalities by ourselves.
The Andoid crazyflie client is currently maintained by Fred from the community. It is mobile Crazyflie client with the most feature. It supports both Crazyradio and Bluetooth link. Using Crazyradio it currently supports the part of telemetry and parameter required to support a couple of deck like the led-ring and buzzer deck and supports updating the firmware. Using bluetooth there is currently no telemetry, parameter or firmware update functionality so no deck support. Development is in progress to support more decks and to bring the bluetooth link to the same functionality as the Crazyradio link. The Android client is written in Java and Fred has developed a Crazyflie Java library that is used in the Android client but that can also be used in any other Java program.
Crazyflie Android client
The iOS Crazyflie client, works on iPhone and iPad. It supports bluetooth link. It does not have any telemetry or parameter support, so no deck control support. It has firmware update support over bluetooth. It has mainly been developed by me with great contributions from the community for, among others, the port to swift. The iOS client is written in swift. The Crazyflie and Bluetooth part of the code could be a good starting point if anyone wanted to make a native mac Crazyflie client.
Crazyflie iOS client
Finally we have a prototype of a Windows UWP client developed by theseankelly. It supports Bluetooth low energy. It currently does not supports any telemetry or parameters. It is working both on Windows phone and on Windows 10 on computer, it is currently the only way to connect a Crazyflie using Bluetooth from a laptop. The windows client supports manual control of the Crazyflie using a gamepad or with gesture using HoloLens. This original set of functionality makes it both the most simple and the most advanced Crazyflie client :-).
If you are interested in developing for any of these client, of by making your own, feel free to make a ticket on the relevant github repo or open a thread in the forum. We migh not have much time to develop for the mobile clients, but we will always be glad to help and guide anyone that wants to implement software in relation with the Crazyflie. The Crazyflie clients (running in a computer or phone) and the Crazyflie firmwares (running in the Crazyflie itself) are open source and in active development, it means that is possible to modify both side, this makes it a great target to experiments and to play around with new ideas :-).
Qualisys is a Motion Capture (Mocap) system manufacturer based in Gothenburg in Sweden. Since we are also based in Sweden, Qualisys have been able to visit us a couple of times and we now have one of their Motion capture system installed at the office. This collaboration allows us to have access to a Mocap system, something we did not have previously. It means that we can better support people using motion capture systems with Crazyflie.
We are currently implementing support for Qualisys in the Crazyswarm project. Crazyswarm currently supports a couple of motion capture system including Vicon and Optitrack, with the addition of Qualisys we and everyone with a Qualisys system will be able to fly swarms of Crazyflie in their mocap.
We are also planning on having a combined booth, Bitcraze and Qualisys, at IROS 2018. We are planning to demo flying both with the Mocap and with the Loco Positioning System. We will keep updating on this when we have more details.
We look forward to this collaboration since it will allow us to use and better support motion capture positioning for the Crazyflie.
We have seen a big interest in flying swarms of Crazyflies and there are many challenges in doing so. The USC ACT Lab has developed Crazyswarm, a collection of software and firmware that allows to fly big swarms of Crazyflie using a motion capture system. This project has been used by USC and other universities to fly the most impressive swarms of Crazyflie 2.0 to date.
We are very happy that we together with Wolfgang and James, the main developer of Crazyswarm, have started to merge the firmware part into the official Crazyflie firmware. Merging the code will have two great consequences: people will be able to use Crazyswarm with a Crazyflie 2.0 running the stock firmware and everybody else will be able to use functionalities that has been developed for Crazyswarm.
There is currently a couple of parts that are in the works. The state controller has been merged already. There is currently some discussion on Github on how to merge the high-level commander, a commander that would allow the Crazyflie to autonomously follow trajectories as well as other high level commands. Finally there will be some work required to adapt the Kalman filter to make it more suited to accepts measurements from a motion capture system. The Crazyflie was not developed as an autonomous platform from the beginning but it is becoming one in big part thanks to the great contributions from the community.
A great thanks to James and Wolfgang for their effort in merging CrazySwarm in the Crazyflie code-base!
Out of stock
Unfortunately we miscalculated how much China slows down during Chinese new year which has caused some products to become out of stock. One of them is the Crazyradio PA which is also causing some bundles to become out of stock as well. The good news is that the products are in transit to the warehouse and will hopefully be back in stock any day now. Until then you can use the “Item out of stock – notify me!” functionality to get notified as soon as the product is back in stock.
During the fall we did two blog-posts (1, 2) about a new prototype named Obstacle Avoidance/SLAM deck, but since then it’s been a bit quiet about it. So we thought it was due for an update! First of all, after a lot of discussions, we decided to rename the deck to Multi-ranger. It better describes what the board does and matches the naming of the Z-ranger. We’ve sent out some samples to customers and so far the response has been great. So we’re pushing forward and preparing for production that’s estimated to begin in March. Below is a picture of the latest prototype.
The biggest change for the final prototype is adding a LDO regulator to power the sensors. We’ve seen that depending on the settings for the sensors they might consume a lot more than when we initially tested. Using the same settings as for the Z-ranger brings the consumption to 90 mA, which together with the Crazyflie 2.0 electronics, comes close to filling the power budget for the Crazyflie 2.0 VCC LDO regulator. Aside from that we’re making some minor changes to simplify production and testing.
