Category: Video

A few weeks ago we wrote about the release of the Multi-ranging deck and the new STEM ranging bundle.

The STEM ranging bundle is a great addition in the classroom for a wide range of students. By combining the Flow deck v2’s time-of-flight distance sensor and optical flow sensor with the Multi-ranger deck’s ability to measure distance to objects, the Crazyflie gets position and spatial awareness.

We have shot a video that shows the bundle in action!

 

To get started with the STEM ranging bundle we have created a guide for the bundle with step-by-step instructions. The code for the demos in the video are available in the example directory of the crazyflie-lib-python project:

  • multiranger_push.py: When the application in launched the Crazyflie will take off and hover. If anything is getting close to the right/left/front/back sensors the Crazyflie will move in the opposite direction. 
  • multiranger_pointcloud.py: When the application is launched the Crazyflie will take off, hover and a 3D-plot will be shown of what is detected by the Multi-ranger deck sensors. By default the left/right/front/back/up sensors will be plotted, but you can also add the Crazyflie position and the down sensor if you like. The Crazyflie can be moved around by using the arrow keys on the keyboard and w/s for up/down and a/d for rotating CCW/CW. For more info see the documentation in the example.

We love feedback so please leave some comments in the field below!

In August we got invited by Marion from ETH Zurich to help out with this years PolyHack, that is organized by Telejob, and which theme was about drones. We really like this kind of events but our reality is that we normally don’t have enough time to participate. For this occasion though we had the opportunity to both have fun and see how our products work when used during an event like this. Two birds with one stone and the decision was made.  Together with one of the main sponsors ELCA, we organized the flying postman challenge:

Drones seem to be the future of post deliveries, but how is it going to work? Join us to reproduce a swarm of drones delivering parcels through a city to have a glimpse at this future!

The challenge the teams got was to deliver as many parcels within 5min in a miniature city, 4m x 4m, using Crazyflies. Since the Crazyflies can’t carry that much payload the parcels was just digital/imaginary but had to be picked up at a pick-up zone. They were allowed to use up to thee Crazyflies simultaneous to increase capacity. For more details checkout the challenge description.

To manage the challenge ELCA developed the CrazyServ which uses a REST API to control Crazyflies, wrapping the high level position commander, and to pick-up parcels. One nice benefit with a server is that it can keep track of which parcels has been picked up and been delivered making the scoring fully automatic.

Bitcraze part in the challenge was to bring drones, technical support and our loco positioning system to make up the 4m x 4m city. Or actually three of them, as there were going to be six teams competing for the victory. The initial information was that the three systems would be installed in separated rooms, far away, but we ended up having them side by side. That left us with some live-hacking, changing from TDoA-2 to TDoA-3 so the anchors would not interfere with each other. We ended up using 12 anchors in total which gave enough precision for the PolyHackers to complete their challenge.

The PolyHack was a success and we had a great time. The winning team in our challenge, Electek Innovation, managed to deliver 19 parcels during the 5min with the use of a “loop” system. Congrats and well done! If you get inspired by this hackaton the CrazyServ is available on github! Together with a e.g. swarm bundle it shouldn’t be to hard to reproduce.

Thanks Telejob for letting us take part of this great event!

 

We have a collaboration with Qualisys, a Swedish manufacturer of top of the line motion capture systems. Similar to us they are a passionate about what they do, are working on high tech products and to make it even better, they are located in Gothenburg, just a couple of hours away by train. If you are not familiar with motion capture systems, it is a system that can track objects with reflective markers in space using high resolution cameras. The precision/accuracy is very good (sub millimeter) and can be used to track more or less anything such as the movements of a human body or the position of a robot, for instance a Crazyflie. The position of a Crazyflie is calculated by the MoCap system and by sending it to the drone via radio, it can fly autonomously.

Qualisys

We are super happy of getting the opportunity to work with MoCap systems and making it an integral part of the Bitcraze eco system. We have already added support in Crazyswarm for the Qualisys system and soon there will be a tab in the Crazyflie python client for basic autonomous flight using a Qualisys system. We will release a passive MoCap deck in the near future that will make it easy to attach reflective markers to a Crazyflie in a well known configuration, see this blog post for more information. Further more we are looking at making an active marker deck that utilizes Qualisys’ active marker technology to both position and identify an object at the same time.

Recently we spent a day in the large lab of Qualisys. We played with the LPS system in a larger set up and experimented with passive MoCap deck configurations and finally tried to fly a swarm.

Martin and Tobias configuring MoCap decks

Unfortunately we ran out of time and we tried to push the envelop a bit too far so we never managed to fly the full sequence without crashes, on the other hand, getting that close in a couple of hours is not too bad. Even though the full swarm did not work out we learned new things and had a lot of fun. Thanks Martin and everyone at Qualisys!

 

If you are looking for a motion capture system and want more information about Qualisys, please do not hesitate to contact us or Qualisys.

In this blog post we will describe one of the demos we were running at IROS and how it was implemented. Conceptually this demo is based on the same ideas as for ICRA 2017 but the implementation is completely new and much cleaner.

The demo is fully autonomous (no computer in the loop) but it requires an external positioning system. We flew it using either the Loco Positioning System or the prototype Lighthouse system.
A button has been added to the LPS deck to start the demo. When the button is pressed the Crazyflie waits for position lock, takes off and repeats a predefined spiral trajectory until the battery is out, when it goes back to the door of the cage and lands.
For some reason we forgot to shoot a video at IROS so a reproduced version from the (messy) office will have to do instead, imagine a 2×2 m net cage around the Crayzflie.

Implementation

As mentioned in an earlier blog post the demo uses the high level commander originally developed by Wolfgang Hoenig and James Alan Preiss for Crazyswarm. We prototyped everything in python (sending commands to the Crazyflie via Crazyradio) to quickly get started and design the demo . Designing trajectories for the high level commander is not trivial and it took some time to get it right. What we wanted was a spiral downwards motion and then going back up along the Z-axis in the centre of the spiral. The high level commander is a bit picky on discontinuities and we used sines for height and radius to generate a smooth trajectory. 

Trajectories in the high level commander are defined as a number of pieces, each describing x, y, z and yaw for a short part of the full trajectory. When flying the trajectories the pieces are traversed one after the other. Each piece is described by 4 polynomials with 8 terms, one polynomial per x, y, z and yaw. The tricky part is to find the polynomials and we decided to do it by cutting our trajectory up in segments (4 per revolution), generate coordinates for a number of points along the segment and finally use numpy.polyfit() to fit polynomials to the points. 

When we were happy with the trajectory it was time to move it to the Crazyflie. Everything is implemented in the app.c file and is essentially a timer loop with a state machine issuing the same commands that we did from python (such as take off, goto and start trajectory). A number of functions in the firmware had to be exposed globally for this to work, maybe not correct from an architectural point of view but one has to do what one has to do to get the demo running :-) The full source code is available at github. Note that the make file is hardcoded for the Crazyflie 2.1, if you want to play with the code on a CF 2.0 you have to update the sensor setting

This approach led to an idea of a possible future app API (for apps running in the Crazyflie) containing similar functionality as the python lib. This would make it easy to prototype an app in python and then port it to firmware.

Controllers

The standard PID controller is very forgiving and usually handles noise and outliers from the positioning system in a fairly good way. We used it with the LPS system since there is some noise in the estimated position in an Ultra Wide Band system. The Lighthouse system on the other hand is much more precise so we switched to the Mellinger controller instead when using it. The Mellinger controller is more agile but also more sensitive to position errors and tend to flip when something unexpected happens. It is possible to use the Mellinger with the LPS as well but the probability of a crash was higher and we prioritised a carefree demo over agility. An extra bonus with the Mellinger controller is that it also handles yaw (as opposed to the PID controller) and we added this when flying with the Lighthouse. 

Going faster

Since the precision in the Lighthouse positioning system is so much better we increased the speed to add some extra excitement. It turned out to be so good that it repeatedly almost touched the panels at the back without any problems, over and over again!

One of the reasons we designed the trajectory the way we did was actually to make it possible to fly multiple copters at the same time, the trajectories never cross. As long as the Crazyflies are not hit by downwash from a copter too close above all is good. Since the demo is fully autonomous and the copters have no knowledge about each other we simply started them with appropriate intervals to separate them in space. We managed to fly three Crazyflies simultaneously with a fairly high degree of stability this way.

As mentioned in an earlier post, this year we are going to exhibit at iROS 2018 in Madrid. Every time we go to fairs and exhibition, it is the occasion for us to work more on integration to put together the latest development into a demo we can show at the event. One of the latest development we will show at iROS is the lighthouse deck.

Work on the lighthouse deck have continued during the summer and we are now at a stage where things are starting to work quite well with Lighthouse V1 base stations. We are quite impressed by the performance: we have measured a positioning noise bellow 1mm. We are flying the Crazyflie using Crazyswarm which allows us to fly smooth trajectory using the high-level controller:

The goal for iROS is to stabilize and push the code in the main Crazyflie firmware repos. We will have a couple of Crazyflie setup with the Lighthouse deck and that we will be able to demonstrate. In the future we are also thinking of making a general purpose tag that could be used with other robots. One of the great advantage of the lighthouse tracking technology is that the position and orientation is available in the receiver, in the robot. This means that, like the LPS, the robots are autonomous and do not require an active data connection with a computer in order to locate themselves.

There is still a lot of challenges and work to be done on the deck. For once, this is currently using HTC Vive lighthouse base station V1, Valve has release the base station V2 that allows to cover much more space for each base station and to use more than 2 base stations in the same system, we plan to implement support for it. We will also need to work on multi-sensor localization and setup procedure. Currently the Crazyflie calculates its orientation using only one lighthouse receiver and requires to be in direct light of sight of both lighthouse, it is possible using more receiver to get a position and orientation with only one base station in sight which will increase the system reliablility. As for the system setup we are still using SteamVR to obtain the lighthouse positions using at least one Vive controller, the goal is eventually to be able to setup a system with the Crazyflie alone, without needing to install SteamVR. All that will most likely be discussed in more details in future post.

If you are attending iROS 2018 feel free to come and meet us at booth #91.

We have now worked a few weeks on the new TDoA 3 mode for the Loco Positioning System. We are happy with the results so far and think we managed to do what we aimed for: removing the single point of failure in anchor 0 and supporting many anchors as well as larger spaces.

 

We finished off last week by setting up a system with 20 anchors covering two rooms down in the lunch area of the office. We managed to fly a scripted autonomous flight between two rooms.

Work so far on the anchors

Messages from the anchors are now transmitted at random times, which removes the dependency on anchor 0 that used to act as a master that all other anchors were synchronized to. The drawback is that we get problems with collisions when two anchors happens to transmit at the same time. Experiments showed that at 400 packets/s (system rate) we ended up at a packet loss of around 15% and 340 TDoA measurements/s sent to the kalman filter for position estimation.  We figured that this was acceptable level and added an algorithm in the anchors that reduces the transmission rate based on the number of anchors around them. If more anchors are added to a room they all reduce their transmission rate to target 400 packets/s in total system rate.

The anchors continuously keeps track of the clock drift of all other anchors by listening to the messages that are transmitted. We know that clocks do not change frequency suddenly and can use this fact to filter the clock correction to reduce noise in the data. Outliers are detected and removed and the resulting correction is low pass filtered. We have done some experiments on using this information and compare it to the time stamp of a received message to detect if the time stamp is corrupt or not, but this idea requires more work.

One interesting feature of the anchors is the limited CPU power that is available. The strategy we have chosen to handle this fact has been to create an algorithm that is efficient when handling messages. A timer based maintenance algorithm (@1 Hz) examines the received data and makes demissions on which anchors to include in the messages in the future as well as purges old data.

The Crazyflie

The implementation in the Crazyflie is fairly straight forward. The biggest change to TDoA 2 is that we now can handle a dynamic number of anchors and have to chose what data to store and what to discard. We  have also extracted the actual TDoA algorithm into a module to separate it from the TDoA 3 protocol. The clock correction filtering algorithm from the anchors has also been implemented in the Crazyflie. 

An experimental module test has been added where the TDoA module is built and run on a PC using data recorded from a sniffer. We get repeatability as well as better tools for debugging and this is something that we should explore further.

Work remaining 

The estimated position in the Crazyflie is still more noisy than in TDoA 2 and we would like to improve it to at least the same level. We see that we have outliers in the TDoA measurements that makes the Crazyflie go off in a random direction from time to time, we believe it should be possible to get rid of most of these.

The code is fairly hackish and there are no structured unit or module tests to verify functionality. So far the work has been in an exploratory phase but we are getting closer to a set of algorithms that we are happy with and that are  worth testing. 

We have not done any work on the client side, that is support for visualizing and configuring the system. This is a substantial amount of work and we will not officially release TDoA 3 until this is finished.

How to try it out

If you are interested in trying TDoA 3 out your self, it is all available on github. There are no hardware changes and if you have a Loco Positioning system it should work just fine. There is a short description on the wiki of how to compile and configure the system. The anchor supports both TDoA 2 and TDoA 3 through configuration while the Crazyflie has to be recompiled to change between the two. The support in the client is limited but will basically handle anchors 0 – 7.

Have fun!

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

Example surface map produced by HoloLens

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!

Mark Debord
Master’s Student
Automatic Coordination of Teams Laboratory
University of Southern California
Wolfgang Hönig
PhD Student
Automatic Coordination of Teams Laboratory
University of Southern California

 

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.