Category: Mechanic

 

I started working with the Crazyflie 2.0 in 2015. I was interested in learning how to program a quadcopter, and the open-source nature of the Crazyflie’s hardware and software was the perfect starting point.

Shortly after, I discovered the world of FPV and the thrill of flying with a bird’s eye view. My journey progressed from rubber-banding an all-in-one camera/VTX to my Crazyflie, to building a 250mm racing quad (via the BigQuad deck), and into the world of Betaflight (including bringing Betaflight support to the Crazyflie hardware).

 

Naturally, the announcement of the Bolt (then known as the RZR) piqued my interest, and the folks at Bitcraze graciously allowed me early hands-on with the product.

This post details my progress towards building out a FPV-style drone on top of the Crazyflie Bolt.

Component List

The FPV community has come a long way since 2015. What once was a very complicated process is now well documented and similar to building a PC (well, with some soldering). For latest details on the specifics of building FPV drones, I recommend resources such as Joshua Bardwell or the r/Multicopter subreddit.

Turns out I had enough components lying around for a 4-inch (propeller diameter) build based on 3S (3 cell) LiPo batteries. Again, there’s nothing special about these parts (in fact they’re all out of date). Take this list as a guide, and do your own research.

  • PDB (Power Distribution Board): This is a circuit board that produces regulated voltages from an unregulated LiPo battery. The Bolt has built-in regulators but is only rated up to an 8A current draw per motor. My 4 inch propellers will certainly draw more than 8A, and so an external PDB is required (plus having dedicated 12V and 5V supplies is nice for peripherals).
  • 4x DYS 1806 Brushless Motors: Brushless motors use magnetic pulses to rotate a motor bell (distinct from brushed motors found on the regular Crazyflie).
  • 4x DYS 20A BLHeli_S ESCs (Electronic Speed Controller): This is a piece of circuitry that accepts a logic-level control signal and applies direct battery power to motor coils to make a brushless motor spin. They have to be rated for the current draw expected by the battery+propeller combination.
  • Tweaker (by Shendrones) Frame: I’ve been wanting to build a quad around this frame, and the large square hole is interesting for the Bolt (more on that later). One thing to keep in mind is this is an ‘H’ style frame. That is, it’s longer than it is wide, so flight will not be perfectly symmetrical. If you’re interested in building a larger Crazyflie and not so interested in FPV, you’ll definitely want a symmetrical ‘X’ style frame.
  • WS2812B addressable LEDs: LEDs are proven to make things better. It’s science.
  • Camera + VTX: For a full FPV setup, you’ll need a camera and a video transmitter. For the most part these run completely independently of the flight controller and so I’ll omit them from this article — what I’ve shown in the picture above is horribly out of date anyway.
  • RX: Radio receiver. For longer range flights and reduced latency it may be a good idea to use an external radio and UART-based receiver with diversity antennas. However, some specific work went in to the Bolt’s antenna design, so I’ll be sticking with the on-board NRF51 and external antenna.
  • Flight Controller: The Crazyflie Bolt!

The Build

Again, there are hundreds of fantastic guides on the web that detail how to build an FPV quadcopter. Instead of trying to create another, here are some notes specific to my Bolt build.

Expansion Decks

Since the Bolt is pin compatible with the Crazyflie, I thought it would be interesting to try and take advantage of a couple existing Crazyflie expansion decks in my build: The LED Ring Deck, the Flow Deck v2, and the Micro SD Card Deck.

The LED Ring Deck

The LEDs were the most hands-on feature to enable. Rather than simply attaching the LED ring inside the frame, I mounted a series of WS2812B lights to the underside of my frame’s arms. The LED Ring Deck consists of 12 LEDs connected in series — so I put three LEDs on each arm of the frame and wired them up in a daisy-chain.

Finally, I soldered the lead to IO_2 (the same that’s used by the LED Ring Deck) on a Breakout Deck.

Since this isn’t the official LED Ring Deck, there’s no OW memory ID. The deck must be force-enabled by specifying a compile flag in your tools/build/make/config.mk file:

CFLAGS += -DDECK_FORCE=bcLedRing

With the custom firmware, the under-arm LEDs work just like the LED Ring Deck (other than the lack of front-facing LEDs).

Micro SD Card Deck

Most popular flight controllers feature flash storage or SD card slots for data logging. The FPV community uses storage to log sensor data for PID tuning and debugging. Naturally, this deck is a good fit on my Bolt build, and requires no additional modification.

Flow Deck (v2)

Remember my interest in the square cutout on my frame of choice? That, and my unorthodox choice to mount the Bolt board below my PDB, means I can theoretically use the bottom-attached Flow Deck to achieve some lateral stabilization while close to the ground. In theory, the VL53L1x ranger should work outdoors thanks to its usage of 940nm light as opposed to 850nm.

Note: This photo also shows the daisy chain wire connecting banks of LEDs in series

Other Build Tips

  • It’s good practice to soft mount flight controllers to minimize transferring motor/prop vibrations into the IMU. I used these to isolate the flight controller from the frame — not perfect, but better than a rigid mount.
  • The receiver antenna must be mounted clear of the carbon fiber frame and electronics. I like to use a heavy duty zip tie and attach the antenna with heat shrink.
  • The Bolt can be powered from a 5v regulator on your PDB, but if you want to take advantage of the VBat sensor it should be powered from the raw battery leads instead. However, most ESCs support active breaking (ability to slow/stop the propellers on demand). Active breaking is known to produce a lot of back-voltage, which can damage some circuits. To be safe, since I’m using a 3S battery (12.6V when fully charged, 11.1V when depleted) I chose to power the Bolt off a regulated 12V supply from my PDB. This way, the PDB’s regulator will filter out voltage spikes and help protect the Bolt. Readings won’t be accurate at the higher range, but what really matters for a voltage sensor is to know when to land.

Results

It works! There is work needed to improve flight, though:

  • Control tuning is required. The powerful brushless motors respond much quicker than brushed motors, and so many of the PID and/or Kalman parameters are too aggressive or just non-optimal.
  • Stabilization with the Flow deck does not work — I haven’t spent much time debugging but my guess is it’s either due to the Kalman tuning, or problems with the VL53L1x depth working outdoors (which also impacts the flow measurements)
  • Betaflight Support: Betaflight has no driver for the BMI088 IMU used on the Crazyflie Bolt or the Crazyflie 2.1.
  • Safety Features: Brushless quads are very dangerous and can cause serious injuries. It’d be good to implement a kill-switch and a more aggressive failsafe in the firmware to prevent flyaways.

All in all, this was an enjoyable project and I’m excited to see some autonomous brushed quads coming out of the Crazyflie community!

This week we have a guest blog post from Joseph La Delfa.

DroneChi is a Human Drone interaction experience that uses the Qualisys motion capture system that enables the Crazyflie to react to movements of your body. At the Exertion Games Lab in Melbourne Australia, we like to design new experiences with technology where the whole body can be the controller and is involved in the experience.

When we first put these two technologies together we realised two things. 

  1. It was super easy to keep your attention on a the drone as it flew around the room reacting to your movements. 
  2. As a result it was also really easy to reflect on and refine ones own movements. 

We thought this was like meditation meditated by a drone, and wanted to investigate how to further enhance this experience through design. We thought the smooth movements were especially mesmerising and so I decided to take beginner Tai Chi lessons; to get an appreciation of what it felt like to move like a Tai Chi student.

We undertook an 8 month design program where we simultaneously designed the form and the interaction of the Crazyflie. The initial design brief was pretty simple, make it look and feel light, graceful and from nature. In Tai Chi you are asked all the time to imagine a flower, the sea or a bird as you embody its movements, we wanted to emulate these experiences but without verbal instruction. Could a drone facilitate these sorts of experiences through it’s design?

We will present a summarised version of how the form and the interaction came about. Starting with a mood board, we collated radially symmetrical forms from nature to match a drone’s natural weight distribution.

We initially went with a jelly fish, hoping to emulate their “push gliiide” movement by articulating laser cut silhouettes (see fig c). This proved incredibly difficult, after searching high and low for a foam that was light enough for the Crazyflie to lift, we just could not get it to fly stable. 

However, we serendipitously fell into the flower shape by trying to improve how we joined the carbon rods together in a loop (fig b below).  By joining them to the main hull we realised it looked like a petal! This set us down the path of the flower, we even flipped the chassis so that the LED ring faced upwards (cheers to Tobias for that firmware hack). 

Whilst this was going on we were experimenting with how to actually interact with the drone. Considering the experience was to be demonstrated at a major conference we decided to keep the tracking only to the hands, this allowed quick change overs. We started with cardboard pads, experimented with gloves but settled on some floral inspired 3D printed pads. We were so tempted to include the articulation of the fingers but decided against it to avoid scope creep! Further to this, we curved the final hand pads (fig  d) to promote the idea of holding the drone, inspired by a move in Tai Chi called “holding the ball”.

As a beginner practicing Tai Chi I was sometimes overwhelmed by the number of aspects of my movement that constantly needed monitoring, palms out, heel out, elbow slightly bent, step forward etc. However in brief moments it all came together and I was able to appreciate the feelings of these movements as opposed to consciously monitoring them. We wanted this kind of experience when learning DroneChi so we devised a way of mapping the drone to the body to emulate this. After a few iterations we settled on the “mid point” method as seen below.

The drone only followed the midpoint (blue dot above) if it was within .2m of it. If it was outside of this range it would float away slowly from the participant. This may seem like a lot, but with little in the way of visual guidance (eg a laser pointer or an augmented display) a person can only rely on the proprioceptive feedback from their own body. We used the on board LED ring on the drone to let the person know at least when they are close, but that is all the help they got. As a result this takes a lot of concentration to get right!

In the end we were super happy with the final experience, in the study participants reported tuning into their bodies when using the drone, as well as experiencing a unique sort of relationship to the drone; not entirely like a pet and also like an extension of the body. We will be investigating both findings from the study through the design and testing of a new system on the Crazyflie. We see this work contributing to more intimate designs for human drone interactions as well as a being applicable to health contexts such as rehabilitation.

We have briefly mentioned the Active marker deck earlier in our blog and in this post we will describe how it works and what it is all about.

The Active marker deck is a result of our collaboration with Qualisys, a Swedish manufacturer of high end optical tracking systems. Optical tracking systems are often referred to as motion capture (mocap) systems and are using cameras to track markers on an object. By using multiple cameras it is possible to calculate the 3D position of the markers and the object they are attached to with very high precision and accuracy. It is common to use mocap systems in robotic labs to track the position and orientation of robots, for instance quadrotors.

Passive markers

The most common marker type is the passive marker, that is reflective spheres that are attached to the robot. By using infrared flashes on the cameras, the visibility of the markers is maximized and it makes it easier for the system to detect and track them. We are selling the Motion capture marker deck to make it easy to attach markers to a Crazyflie.

To get the full pose (position, roll, pitch, yaw) of a robot, the markers must be placed in a configuration that makes it possible for the mocap system to identify the orientation. This means that there must be some asymmetry in the marker positions to understand what is front, back, up, down and so on.

With a swarm of Crazyflies, unique marker configurations makes it possible to distinguish one individual from another and track all drones simultaneously. With a larger number of robots it becomes cumbersome though to place markers in unique configurations, and one approach to solving this problem is to have known start positions for all individuals and keep track of their motions over time instead. This solution is used in the Crazyswarm for instance and all Crazyflies can use the same marker configuration in this setup. Another approach is to make it possible to distinguish one marker from another, enter the Active marker deck.

Active markers

It is possible to use infrared LEDs instead of the passive markers, this is called active markers. The LEDs are triggered by the flash from the cameras and they are easily detected as strong points of light. Since they are emitting light they can be detected further away from the camera than a passive marker and the smaller physical size also keeps them more separated when they are far away and only a few pixels are available to detect them in the camera.

Furthermore Qualisys has a technology that makes it possible to assign an id to each marker and that enables the tracking system to identify individual markers and thus uniquely identify individuals in a swarm. With different IDs on the markers, there is no need have asymmetrical configurations and the marker layout can be the same on all drones. It also reduces the risk of errors in the estimated pose, since there is more information available.

The deck

The Active marker deck is designed to go on top of the Crazyflie and has four arms with one LED each. The arms are as long as possible to maximize the signal/noise ratio in the cameras, while still short enough to be protected from crashes by the motors. There is a STM32 F0 on the deck that takes care of the LEDs and handling of IDs and the main Crazyflie CPU does not have to spend any time on this.

The status of the deck is that the hardware is fully functional (we might want to move something around before we produce it though) and that there is a basic implementation of the firmware. IDs are assigned to the markers using parameters in the standard parameter framework in the client or from a script.

We will start production of the deck in the near future and it will be available in the store this autumn. Qualisys added support for rigid bodies using active markers in V2019.3 of the QTM tracking software.

We attended the Innovation Week at Lund University on Thursday last week. Primarily we wanted to talk to students and possibly find future colleagues (yes, we are hiring) but it was also a good opportunity to get some demo time with the Lighthouse positioning system.

The demo setup. A bit blurry, sorry!

As mentioned in an earlier blog post, we are going to ICRA in May and we have started to think about what to demo. The main feature will of course be the Lighthouse deck. The setup at Innovation week also served the purpose of a first iteration for the ICRA setup.

We reused an old cage that we created for another fair a couple of years ago, built from a garden tent. It turned out to be fairly wobbly and a bit heavy (steel tubing) considering we will bring it in our luggage to Canada. We probably have to rethink the construction a bit and see if we can change to aluminium.

We put the Lighthouse base stations on tripods, which worked like a charm in our flight lab. We found that we had a lot of problems calibrating the system, not to mention flying the Crazyflie, at the Innovation week fair though. It turned out that the floor was not as stable as one might expect and that the tripods were swaying when people walked by. We solved the problem by adding a tube to the top of tripod that was pushed against the ceiling and thus minimizing the movement. Experience from the real world is always useful!

The general idea for the demo at ICRA is to automate as much as possible to give us more time with visitors. With the high precision of the Lighthouse system, it should be easy to land the Crazyflies on Qi chargers to avoid changing batteries. We hope to set up 6-8 Crazyflies where one is always flying while the others are charging, and have the possibility to temporarily fly more Crazyflies for small swarms. It is still just ideas and we will not see the end result until we are at ICRA, but it will be fun to build!

Ever since the Raspberry-pi zero was released we wanted to find-out what it would take to fly one with the Crazyflie 2.0. One immediate issue is the size and weight of the R-Pi-Zero. It is just a bit to big and heavy to make it work without modifying the Crazyflie 2.0. Also it requires 5V power which is something the Crazyflie 2.0 doesn’t provide if USB isn’t connected. Actually the R-Pi-Zero works well down to ~3.6V but this is still too high to reliably run directly from a single LiPo cell. So to begin with we created a Raspberry Pi Zero power deck. It is reusing the same step-up/step-down (STBB1) as used on the LED-ring to make things simple and the output is set to 3.8V. Other than that the UART and the I2C interfaced has been connected so that the raspberry pi zero could control the Crazyflie.

The raspberry pi zero would then be soldered to the deck with 0.1″ header pins. The result can be seen below and the power part works well. We chose to solder the deck header pins to the deck, instead of using the female deck connectors, to make it more sturdy. Another thing we did was fitting a Pi-camera using a 3D printed mounting bracket we designed. We think this is one of the interesting use cases, to run computer vision or maybe neural networks :-).

Well unfortunately this only solves the first part, powering the R-Pi-Zero from the Crazyflie 2.0. Next step will be to modify the Crazyflie 2.0 with bigger motors/props so that is can carry it for a decent time. So story to be continued…

The Crazyflie 2.0 has been flyable in MoCap systems such as Qualisys, Vicon or Optitrack for quite a while thanks to the Crazyswarm project. For the MoCap systems to be able to track the Crazyflie it needs to be fitted with reflective markers. These can be attached on e.g the motor mounts which in some cases might be the best solution, however we also liked the idea of creating a deck where the markers easily can be attached and in many, repeatable configurations, that is why we created the Mocap deck. This deck is now soon to be released but before we start manufacturing it would be great to get some feedback.

The deck has M3 sized holes which is spaced on a 5mm grid. The deck also has footprints for two optional push buttons that can be used to e.g. trigger a take-off or start of a demo. And as the battery holder deck, which has no electronics, so this can be mounted upside down for better fitting of the markers, if the buttons aren’t mounted of course.

We are collaborating with Qualisys, also based in Sweden (in Gothenburg), to make the Crazyflie more moCap friendly and make it easy to use together with the Qualisys system as well as other mocap systems. Qualisys will provide the markers for the moCap deck.

Qualisys and Bitcraze are exhibiting together at IROS in Madrid where we will show some awesome demos with a moCap system as well as other position technologies and we are also hoping to fly a Crazyswarm. We will publish more information when we get closer to the conference.

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

 

During the fall we did two blog-posts (12) 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.

We’ll keep you updated on the progress!

We have been lucky get the opportunity to use a motion capture system from Qualisys in our flight lab. The Qualisys system is a camera based system that is using IR-cameras to track objects with sub-millimeter precision! The cameras are designed to measure the position and track small reflective marker balls that are fixed to the object to be tracked with high accuracy. By using multiple cameras shooting from different angles it is possible for the system to calculate the 3D position of a marker in space. By mounting multiple markers on an object the system can also identify the object as well as its orientation in space. Very cool!

We have started to look at how to add support in our ecosystem for the Qualisys system as well as other “external” positioning systems, external in this context is systems that calculate the position outside the Crazyflie. There is already great support for external positioning in the CrazySwarm project by the USC-ACTLab, but we are now looking at light weight support in the python client. We are not sure what we will add but ideas are on the lines of viewing an external position in the client, feed an external position into the Crazyflie for autonomous flight and maybe a simple trajectory sequencer.

MoCap Deck

We have also started to design a MoCap Deck to make it easy to mount reflective markers on the Crazyflie. Our design goals include:
* light weight
* easy to use
* support for multiple configurations to enable identification of individuals
* the possibility to add a button for human interaction

The current design of the MoCap Deck

The suggested design of the MoCap Deck

Any feedback on the MoCap Deck and ideas for functionality to add to the client is welcome! Please add a comment to this blog post or send us an email.

We will write more about the Qualisys system later on, stay tuned!

Modular robots can adapt and offer solutions in emergency scenarios, but self-assembling on the ground is a slow process. What about self-assembling in midair?

In one of our recent work in GRASP Laboratory at University of Pennsylvania, we introduce ModQuad, a novel flying modular robotic structure that is able to self-assemble in midair and cooperatively fly. This work is directed by Professor Vijay Kumar and Professor Mark Yim. We are focused on developing bio-inspired techniques for Flying Modular Robots. Our main interest is to develop algorithms and controllers for self-assembling modular robots that can dock in midair.

In biological systems such as ant or bee colonies, collective effort can solve problems not efficient with individuals such as exploring, transporting food and building massive structures. Some ant species are able to build living bridges by clinging to one another and span the gaps in the foraging trail. This capability allows them to rapidly connect disjoint areas in order to transport food and resources to their colonies. Recent works in robotics have been focusing on using swarm behaviors to solve collective tasks such as construction and transportation.Ant bridge. Docking modules in midair offers an advantage in terms of speed during the assembly process. For example, in a building on fire, the individual modules can rapidly navigate from a base-station to the target through cluttered environments. Then, they can assemble bridges or platforms near windows in the building to offer alternative exits to save lives.

ModQuad Design

The ModQuad is propelled by a quadrotor platform. We use the Crazyflie 2.0. The vehicle was chosen because of its agility and scalability. The low-cost and total payload gives us an acceptable scenario for a large number of modules.

Very light-weight carbon fiber rods connected by eight 3-D printed ABS connectors form the frame. The frame weight is important due to tight payload constraints of the quadrotor. Our current frame design weighs 7g about half the payload capability. The module geometry has a cuboid shape as seen in the figure below. To enable rigid attachments between modules, we include a docking mechanism in the modular frame. In our case, we used Neodymium Iron Boron (NdFeB) magnets as passive actuators.

Self-Assembling and Cooperative Flying

ModQuad is the first modular system that is able to self-assemble in midair. We developed a docking method that accurately aligns and attaches pairs of flying structures in midair. We also designed a stable decentralized modular attitude controller to allow a set of attached modules to cooperatively fly. Our controller maximizes the use of the rotor forces by generating the maximum possible moment.

In order to allow the flying structure to navigate in a three dimensional environment, we control thrust and attitude to generate vertical and horizontal translations, and rotation in the yaw angle. In our approach, we control the attitude of the structure in a decentralized manner. A modular attitude controller allows multiple connected robots to stably and cooperatively fly. The gain constants in our controller do not need to be re-tuned as the configurations change.

In order  to dock pairs of flying structures in midair, we propose to have two flying structures: the first one is hovering and waiting, meanwhile the second one is performing a docking action. Both, the hovering and the docking actions are based on a velocity controller. Using a velocity controller, we are able to dock multiple robots in midair. We highlight that docking robots in midair is one of the fastest ways to assemble structures because the building units can rapidly move and dock in three dimensional environments. The docking system and control has been validated through multiple experiments.

Our system takes advantage of robot swarms because a large number of robots can construct massive structures.

 

 

This work was developed by:

David Saldaña, Bruno Gabrich, Guanrui Li, Mak Yim, and Vijay Kumar.

 

Additional resources at:

http://kumarrobotics.org/

http://www.modlabupenn.org/

http://davidsaldana.co/