Category: Video

A couple of weeks ago we played with recording and retracing trajectory directly from the Crazyflie using Loco Positioning System. The result was quite nice and resulted, a first for us, in a fully autonomous Crazyflie, no computer or controller required:

We decided to expand on this experiment for our demo at ICRA. We have modified the retracing code to accepts multiple modes, including running pre-programmed sequence. The plan for the demo is to have Crazyflies that can:

  • Record and retrace a manual trajectory
  • Record and replay in a loop a manual trajectory
  • Play a pre-defined trajectory in a loop
  • Land automatically when the battery level is low

With this we should be able to demonstrate quite well the capabilities of both the Crazyflie and the Loco Positioning system, and since we do not require a computer in the loop it simplifies a lot running the demo. Of course we keep the possibility to connect the Crazyflie with the Crazyflie client and with ROS while the crazyflie is flying.

Having a completly autonomous Crazyflie is also new to us and it brings its share of problems: how to we choose the working mode and how to we stop the flight if something happens (things tends to happen …).

To solve the former we have made a button deck that adds 2 push-button to the Crazyflie. One means “Start autonomous sequence”. The second means “Record trajectory”. If the recorded trajectory is a loop (if the end point is close to the start point) then the loop is played back as soon as the crazyflie is dropped, otherwise Crazyflie retraces the trajectory and stop.

We solved the later problem by making an autonomous emergency stop button that sends a radio watchdog signal. If the signal stops to be sent or if an emergency stop signal is sent (ie. by pressing the button), the Crazyflie will stop all motors and drop. The button is implemented using a Raspberry pi, a Crazyradio and an Arduino to interface the button:

If you are curious about code, we have created a github repos where we push all code we are making for this demo. As usual, this conference is an opportunity for us to hack new functionalities, though not everything can be done in the master branch. Later some things can be merged, others (like the retrace trajectory recorder/player that looks more like a user app.) will need much more though if we want to merge it in the Crazyflie firmware.

At Bitcraze we have some history with trying to fly our Crazyflie autonomously. The most recent step is the Loco Positioning System that allows us, and you, to fly in a full room. The Loco Positioning system has boosted development of advanced algorithms for onboard position estimation and control.

Our earlier attempts where mostly based on different kinds of cameras, either a 3D camera like Kinect or regular webcams. Though, at that point, we only had the camera for position estimation and where doing the position control on the PC and not onboard the Crazyflie. This has the disadvantage to be brittle and requires a very high quality positioning from the camera: any frame where we loose the Crazyflie has a huge impact on the control behavior since the position controller relies exclusively on the camera detection.

With the Kalman filter and onboard position controller, the Crazyflie can now handle lost position information for at least a couple of seconds without big problems. This has the potential of making webcam-based position detector much more robust!

To test this theory we have grabbed the 2 years old crazyflie-ar-detector from the dawer github, updated it to OpenCV 3.2.0, and fed the position output to the Crazyflie 2.0 external position port. The crazyflie-ar-detector program is using ZeroMQ to communicate position and so we made a simple external position tab for the Crazyflie Client that receives position from ZeroMQ and sends it to the connected Crazyflie.

Using the new position-hold mode recently introduced in the client we can test and fly the Crazyflie under the webcam. We have taken a short video to show the performance. The result is promising and we will continue to play with ways to fly the Crazyflie autonomously.

Early on when we started to work on the Loco Positioning system, we came up with an idea of a Crazyflie autonomously flying into a light box, positioning it self for a few product pictures and then flying out again. The positioning system is now pretty mature and close to leave Early Access and this Friday we finally got around to do it. In this blog post we will share what we did and it also doubles as a brief howto on how to set up the system and fly a simple autonomous sequence.

We used a Crazyflie with a Loco Positioning Deck and eight Loco Positioning Anchors in our setup. Six anchors would have been fine too, but we happened to have eight in our flight lab.

When working with the Loco Positioning system the first step is always to make sure the anchors are set up correctly. We had an experimental version of the anchor firmware so we started out by pulling down the latest stock version of the source code and compiled it into a .dfu file. After that we fired up the brand new lps-tool that is used to flash firmware and configure the anchors. The anchors must be connected with a USB cable to the computer but the lps-tool reduces the flashing and configuration into a few clicks. When all anchors were updated we were ready for the next step.

The positions of the anchors are stored in the anchors them selves and the position is transmitted to the Crazyflie as a part of the ultra wide band messages used for measuring the ranges to the anchors. This way, the Crazyflie gets both anchor positions and ranges in the same process and has all the information needed to calculate its position. The second step is thus to store the positions in the anchors. In our “flight lab” we have fixed mounts for the anchors with known positions, so we could skip measuring the physical positions of the anchors.

We are working on making it possible to remotely configure the anchors to reduce the need to physically connect to them, and the position can now be set from the Crazyflie Client. We simply opened the “Loco Positioning” tab in the client, connected to a Crazyflie (with a Loco Positioning deck mounted), entered the anchor positions and hit the “Write to anchors” button. A few seconds later the anchor positions in the graphs were updated to indicate that the positions have been written to the anchors and then subsequently sent back through the ultra wide band messages to the Crazyflie.

Step three is to verify that the system is working as expected. First thing is to check that we did not mix the anchors up when configuring or placing them. In the “Loco Position” tab in the client, click the “Anchor Identification” button. In this mode anchors are lit up in the graphs when the Crazyflie gets close to them in the physical world. We went from anchor to anchor with the Crazyflie and checked that the correct anchor lit up on the screen. When confident that all was good we changed to “Position estimate” mode and verified that the estimated position matches the physical position of the Crazyflie. We have found that it can be very hard to understand, for instance that two anchors have been mixed up, by looking at the estimated position and that the “Anchor Identification” step simplifies the setup.

At this point we had a fully functioning Loco Positioning system ready for autonomous flight!

Now it was time to script a sequence. The easiest way to script a sequence is to start from the autonomousSequence.py example. Our intern Alfred took over at this point, he updated the uri to the correct settings and crafted a sequence to take off, fly into the light box, wait a while and then fly back out for a stylish landing in his hand!

Now we were ready for the actual photo shoot and Björn came down with the camera to shoot the product pictures. We hope you enjoy the results!

We have spent most of our time working on the two way ranging in the Loco Positioning system lately, mainly on features that are not directly related to the actual ranging but such as making it easier to configure and upgrade the anchors. As a result we have not exercised the TDoA mode in a while, so on our Fun Friday we wanted to play a bit with that and try to fly a small swarm with some sort of coordinated autonomous flight.

Setup

We have a Loco Positioning system set up in the basement, we call it the flight lab to make it sound more fancy! The setup has been 6 anchors with three anchors in the ceiling and three on the floor, configured as triangles pointing in opposite directions. When using two way ranging that is fine as the positioning works pretty well outside the volume defined by the anchors. For TDoA on the other hand, the accuracy of the estimated position degrades rapidly when you go outside the convex hull. We decided to add two more anchors (to a total of eight) and arrange them as the corners of a box. A few hours and mounting/cabling later we were ready to try it out.

We modified the swarmSequence script to suite my (limited) space and set it up to fly four Crazyflies in a square, moving them to the next corner of the square every 5 seconds. Next problem was to find 4 working Crazyflies and Loco Positioning decks. We have a few Crazyflies lying around but a fair number of them have been modified in one way or another but finally we had the hardware we needed and could run the script. After a couple of failed tries (out of battery and such) we shot this video

Lessons learned

So what did we learn from this exercise? Adding two more anchors and changing anchor positions improved the positioning significantly. We have seen earlier that TDoA is less accurate than two way ranging, but better anchor positions reduces the problem. We could also fly the swarm using our example python script (not using ROS) without too much work and trouble, even though the TDoA mode still is very experimental.

In this flight we used the stock controller and just moved the set point to the next desired position for each copter. We are really looking forward to try out the improved controller and trajectory planning that we showed at Fosdem in combination with the TDoA mode, we think it will improve the performance a lot!

For the third year some of us from Bitcraze visited Fosdem, the biggest open-source European conference. Like the other years we enjoyed being there a lot and we had a great time hanging-out with community members like Fred.

Fred presented a great lightning talk about the news in the Crazyflie galaxy, the video and slide are already available. 

Arnaud talked about the Loco positioning system. The talk and the demo, went well. Unfortunately the video from the talk is not available yet, we will tweet it and add it to this post as soon as it is online.

The Loco Positioning talk was a great opportunity for us to test the most recent bleeding edge additions to the Crazyflie autonomous algorithms. We flew the new non-linear controller from Mike Hammer using trajectory generation from Marcus Greiff. The non linear controller uses setpoints not only of position but also of velocity and acceleration to control the Crazyflie. This is where trajectory generation is useful: if you can generate a trajectory and calculate position, velocity and acceleration over time, you can feed all this information to the controller and the controller will be able to do a much better job following your trajectory. This enabled us to fly the Crazyflie fairly aggressively the week before the FOSDEM talk:

In this video the Crazyflie is accelerating to about 2g continuously to keep the trajectory. We were a bit concerned to fly such aggressive maneuvers in public without more testing so we designed a slightly safer demo the night before the talk in our hotel room:

This trajectory was successfully flown in the demo and shows the performance of this new controller. There has been a lot happening with the Crazyflie control algorithms lately: Marcus, Mike and Wolfgang have all made new controllers and Marcus has developed an on-board trajectory generator. There is still some work required in the firmware architecture to merge these into Master, but we hope this can be done in the coming weeks. Follow the Crazyflie firmware commits and github tickets if you are interested in the progress.

During the fall of 2016 fashion designer Maartje Dijkstra have in collaboration with music producer Beorn Lebenstedt (Newk) and engineer Erik Overmeire been working with the creation “TranSwarm Entities”, a dress made out of 3D prints accompanied by autonomously flying Crazyflies. The project was made during the Fashion Fusion Lab, a three-month workshop in which selected teams got to work on their fashion concepts. Maartje and her team used our Loco positioning system to enable 4 Crazyflies to do a “dance” around the dress during the show.

 

Copyright Fashion Fusion

Here is how Maartje describes the creation:

“The sculptural high fashion dress is totally build up out of small fragments (bird skulls), like cells building an organism.The parts are manual 3D printed and after printing all connected together by hand with polyester wires and green leather. The technology part is integrated in a special way. 4 small drones, that have given the same black 3D printed appearance as the dress, fly up from places inside so it looks like parts of the dress are flying away.The drones fly on the beats and melodies of music producer Newk around the model creating a little swarm. The shoes are digital 3D printed but finished manually.”

The finalists from the Fashion Fusion Lab got to compete during the Berlin Fashion Week at the “Fashion Fusion Challenge” and we are happy to announce that Maartje together with her team got the third place

We at Bitcraze are very happy for Maartje and her team and think it’s very exciting to see the Crazyflie 2.0 and the Loco positioning system being used in such a different context. It shows again the potential for future applications and how versatile the Crazyflie and the Loco positioning system is. 

Here is a video showing the dress:

This week we got an email from David Gómez, a research scientist at Multi-Agent Autonomous Systems Lab, Intel Labs. He and his team have done some cool work on trajectory planning in cluttered environments that we think needs to be shared. For their research they used the Crazyflie 2.0 which we think is even cooler :-). Watch the video to see how the Crazyflie 2.0 find its way though narrow corridors and obstacle dense environments.
 

 

If you are interested in the full paper, “A Hybrid Method for Online Trajectory Planning of Mobile Robots in Cluttered Environments”, you can find it under Crazyflie 2.0 publications in the research section.

This year we decided to do a short Merry Christmas video. The video was done during one chaotic evening last week were both time and technology seemed to be against us. We are anyway happy with the result which we hope will spread some Christmas joy!
 

 
PS. All flights, except the first take-off, where autonomous using the loco positioning system. Code and documentation to come later ;-). DS

Merry Christmas from all of Bitcraze!

Most of the time we have a few prototypes lying around that we’re working more or less on. Sometimes some of these make it into a product if we feel that they might be useful or fun for the community, like for instance the SD-card. Now it’s time for another prototype to be moved to manufacturing, a deck with VL53L0x laser ToF distance sensor.

On the Crazyflie 2.0 (and Crazyflie 1.0 10-DOF) we have a pressure sensor mounted to help control the altitude of the platform. Since air pressure is moving around a lot and the measurement is noisy it’s been very hard to get a rock-solid altitude hold working (although it’s getting closer). Already back when ST released the VL6180X we were looking at it, but the range was too short (10cm max). So when ST released the VL53L0x which has longer range (200cm max) we though this might be a good deck for the Crazyflie 2.0.

So we have a working prototype and thanks to stephanbro and Marcus Grieff we also have the firmware to use it with the Kalman filter. We are currently working at making it work together with the pressure sensor with the current altitude-hold mode.

Currently we’re working on verifying the hardware to make sure the power supply is good enough for it, but then the next step is production. Hopefully it will be available in a couple of months 🙂 Below is a picture of the current prototype.

VL53

Last week we reached a milestone for our Loco Positioning System: we got 5 Crazyflie 2.0 to fly in a swarm with Time Difference of Arrival measurements. This is a great step closer to making the LPS leave the early-access state.

Until now, positioning has been done using a method called Two Way Ranging (TWR). The advantage of TWR ranging is that it allows us to easily get ranges to the anchors by actively pinging them in sequence. Based on these ranges we can then calculate the current Crazyflie position and control the Crazyflie to move to a wanted position. The big drawback though is that since each Crazyflie has to actively transmit packets to ping anchors, flying many Crazyflie means sharing the air and so the more we want to fly the less ranging each Crazyflie can do. In other words: it does not scale.

TDoA measurement consist of measuring the difference of flight time between packets coming from different anchors and this is harder to achieve since the anchor clocks must be synchronized to each other. The killer feature of TDoA is that it can be implemented using unidirectional packet sent from the anchor system and received by the tag/Crazyflie. It means that as soon as you get one Crazyflie flying with TDoA, you can get as many as you want since the Crazyflies do not have to transmit anything.

This is what happened last week: on Thursday evening we got 1 Crazyflie to fly with TDoA measurements. On Friday we tried 3 and then 5 without much effort. It was just matter of modifying the ROS launchfile to connect more crazyflies, a copy-paste operation.

Then

There still seems to be a margin for progression to get even more stable flight with TDoA and we are also working on making the LPS and Swarm work with our Python client which will make it easier to use outside a robotic lab.

If you want to try the (very experimental!) TDoA mode with your loco positioning system we have documented how to get it to work on the wiki.

Thanks a lot to the growing community that is supporting us and allow us to move faster towards a Crazyflie swarm.