Crazyflie Bolt: FPV meets Autonomy

2020-01-13 | Sean Kelly | 4 Comments

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).

: FPV for Beginners ;)

: BigQuad Build

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 Again, there’s nothing special about these parts (in fact they’re all out of date).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!

Production Tests

2019-12-16 | Arnaud | Leave a comment

We are currently finishing production test design for a couple of expansion decks and we figured we never wrote about it and about the more general board production process. In this blog post we wanted to talk a bit about how we test boards in the productions phase, taking as an example the forthcoming active marker deck.

When finalizing an electronic board, we send to the manufacturer documentation that allows to manufacture & assemble a, hopefully, functional board. Although we assume that the individual components are in working order, the problem is that the assembling is not always perfect, so we need to check that everything we do is actually working,. This is what the production test is solving.

The first thing is to find out what to test, for that we need a strategy. The strategy we have been using is to test every step where we have modified or work on: for example we will test all the connections we have soldered in the manufacturing process. We will normally not test all the functionalities of ready-made module. For example, following this strategy, we will usually test all communication interface we have cabled, but we will not test all functionalities of a microcontroller we solder on the board, these are deemed to be already tested and working by the microcontroller manufacturer. This step usually end up with an annotated schematic:

Once we know what to test and roughly how to test it, we document a test rig that will be able to run the tests automatically. Some tests are generic and applicable to all our boards, for example we do test voltages with a multi-meter on every board that has a regulator. Some tests are very board specific. For example, for the active marker deck we want to test IR LEDs and an IR detector, we define a test rig that has reflector to reflect the LED to the detector and we will use the onboard detector to test the LEDs:

Simple block diagram of the test rig for the ActiveMarker Deck

We are normally using a Crazyflie on all our test rig, since it is usually possible to test all functionality from the deck port. We also try as much as possible to integrate the test software into the real software. For the active marker deck it meant adding 38KHz modulated output mode to the LEDs in order to emit a signal detectable by the detector, which will make it to the final firmware. Finally, we have a test software, running on the test computer, that uses the Crazyflie python lib to talk to the Crazyflie and run the test. The last step of all the test is to write the deck One Wire identification memory so that it can be detected by a Crazyflie.

Screenshot of the test program for the test engineer

From these specification, the manufacturer can then build a test rig and start testing boards, non-passing board will be re-worked until they pass or discarded.

Test rig for the Multi-ranger expansion deck

What we have learned in our years at Bitcraze is that testing phase is the most important part of the development process of PCB. Therefore, the earliest we already start thinking about the production tests in the board design, the more smooth the final phase of production of our new products will be.

Crazyflie Bolt in store

2019-12-10 | Tobias | Leave a comment

The Crazyflie Bolt control board

After a couple of delays we are happy to announce the Crazyflie Bolt is now stocked and ready to ship out. For those of you that are new to the Bolt, it is basically a Crazyflie 2.1 control board, but built to fit a bigger package. We have blogged about it a couple of times before, so if you would like to catch up you can start from the first idea, to maturing and finally changing name from RZR to Bolt. Another way to describe the Bolt is: Crazyflie 2.1 + Big-quad deck in one which doesn’t hog any deck expansion pins. Thus combinations such as Bolt + Led-ring + Lighthouse-4 is now possible or e.g. Bolt + Flow v2 + LPS.

Keep in mind that the Bolt is an early access product so you will most likely have to dig in to the code to hard-code PID-tuning parameters etc. Also trowing a warning finger, heavier drones can be very dangerous so be sure to keep safe!

The Crazyflie Bolt is delivered as a stand alone control board. Frame, motors, propellers and battery needs to be added, for details check out the wiki. Unfortunately we don’t have a good reference kit to recommend at the moment. If you happen to have built a good one, please share.

Crazyflie Bolt Kit

Sneak peak of the AI-deck

2019-10-07 | Tobias | 10 Comments

As pointed out in Daniele’s blog post about the PULP-DroNet we are collaborating on a AI-deck built around the new GAP8 RISC-V multi-core MCU. In the blog post you can find all the details around DroNet while here we will talk a bit about the AI-deck hardware. The AI-deck is similar to the PULP-Shield but with some optimizations. One of the HyperFlash memory spots has been removed, the communication interface slimmed down and a ESP32 (NINA module) has been added for WiFi connectivity.

So all together this a pretty good platform to develop low power AI on the edge for a drone.

Features:

GAP8 – Ultra low power 9 core RISC-V MCU
Himax HM01B0 – Ultra low power 320×320 greyscale camera.
512 Mbit HyperFlash and 64 Mbit HyperRAM
ESP32 for WiFi and more (NINA-W102)
2 x JTAG for GAP8 and ESP32

Currently we are doing the final testing of the hardware and hopefully we will launch production in the end of October. If production goes according to plan we hope we can offer it as an early access product just before X-mas. Make sure to come back and check the blog for more information about the progress as well as pricing.

The Active marker deck

2019-09-02 | Kristoffer Richardsson | Leave a comment

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.

Summer 2019 Wrap Up

2019-07-01 | Kimberly McGuire | Leave a comment

Summer is here and temperatures are rising. Since many of us will be on holidays, we will focus this quarter on a special summer clean up! See here what we are working on:

Fixing issues: This time we are aiming to close many of the issue tickets in our Github repositories, so that after the summer everything will run much more smoothly (we hope!). Definitely our test rig will come in very hand to sniff out more issues in terms of radio communication as well. You can help as well! Everybody who is using and developing on with the STM-firmware, NRF-firmware or python library, or anything else and is noticing issues, please make a ticket in that same Github repository (if you are familiar with the code) or post about it on our forum (if you do not know exactly what is going on). Together we can make the code better.

Lighthouse calibration: In March we released our lighthouse deck for positioning with the HTC Vive base stations. We did feel that the setup process could be improved further, since currently, the Crazyflies’ firmware must contain hardcoded information of the Steam VR’s base station position. We will try to apply the factory calibration direct from the Base stations itself. This will enable us to do 2 additional things: (1) The Crazyflie with the LH deck itself could be used to setup the Lighthouse system, so that SteamVR would not be necessary anymore. (2) Only 1 base station is needed for positioning instead of 2, which will improve the robustness in case of loss of visual-line-of-sight of one base station.

Documentation: We try to provide all the possible information for everybody to be able do anything they want with their Crazyflie. But with high flexibility comes great responsibility…. for proper documentation! We are planning to restructure all of our media outlets and try to improve the flow and level of detail for our users. We hope to make it easier for beginning developers to get started and more advanced developers to gain better understanding of the system in order to implement their own awesome ideas. So our very first step is to restructure and clean up the Bitcraze wiki and see where we can add more content.

Products: We have a lot of products coming out in the 2nd half of the year
- AI Deck: We are working hard to get the AI deck all ready for production and we are estimating that they will be available for early access in late autumn. Keep a look out on our forum for regular updates on the progress!
- Lighthouse breakout board: We made our first working prototype of the lighthouse breakout board, which should make it easier for the lighthouse positioning system to also work on other platforms than the Crazyflie.
- Active Marker Deck: We are very much on on track with the Qualisys active marker deck! It should be available in the Autumn.
- Crazyflie Bolt: This has been send off to production for the early access version, which should be available in the Autumn!

The RZR becomes the Bolt

2019-06-17 | Tobias | 2 Comments

We have now come to a the point were we will start manufacturing of the Crazyflie Bolt, Formally known as the RZR. You might wonder why we changed the name… Well the RZR more implies it is a Racer quad and it really isn’t. This is mainly because of the design in power distribution which is limited to around 8A per motor. However by using your own PDB it will work well for that too. But that is not the intention, it is more intended to have the strengths of the Crazyflie 2.1 but in a slightly bit bigger package. Therefore we wanted a better name for it and after a brainstorming session we came up with the name, Bolt. Both as it is a Crazyflie building block, a bolt used to fasten things, but also because it has the potential to be fast, as in a lightning bolt. Great name right :-)

The CF-Bolt development has been pushed back many times because of other more promising products, but finally it is getting here. If things goes according to plan, the Crazyflie Bolt should be in our shop in Aug-Sept. If you want to read up on the history and what it is all about read about the first flight to the almost-final prototype here.

A quick recap of the features:

Fully compatible with the CF2 firmware, expansion decks as well as radio.
Connectors to attach motor controllers (also possible to solder though) so it is easy to build and repair.
Power distributions built into controller board. (Max ~8A per motor controller) with XT30 connector.
Motor controllers can be switched off by the system (MOSFET) so the system can go into deep sleep and only consume around 50uA.
Voltage input 1S-4S (3V to 17V).
Standard mounting (M3 mounting holes spaced 30.5mm in a square).
External antenna for increased range.
SPI connected IMU (BMI088) for minimum latency.

Making a radio test-bench for Crazyflie2

2019-06-03 | Arnaud | 2 Comments

While running our ICRA demo, we came across a bug in the Crazyflie python-lib radio handling, limiting the number of Crazyflie that could be controlled using one Crazyradio PA. Communication with many Crazyflies is crucial as flying swarms is becoming more of an interesting topic for research and education. So we decided to take the problem at hand and create a radio test-bench:

To make the test-bench we have attached 10 roadrunner boards to a plank of wood together with USB switches that can provide enough power to the roadrunners. We used the roadrunner because it is mechanically easier to use in this context and it has an identical architecture to the Crazyflie 2.1 when it comes to the radio implementation.

Initially we will use the test-bench to run test scripts that pushes the communication to its limits and that consistently test the communication stack functionalities. This should allow us to find bug and verify that we solve them as well as discovering and documenting limitations.

Eventually we want to connect a raspberry-pi to the test-bench and run tests for each commit and pull-request to the crazyflie-firmware, crazyflie2-nrf-firmware and crazyflie-lib-python projects. This will guarantee that we do not introduce new limitations in the communication stack. The test-bench will also be very useful in implementing new functionalities like direct crazyflie-to-crazyflie P2P communication.

As a final note, the Crazyswarm project is not affected by the Crazyflie-lib bug since it is using the C++ implemented crazyflie-ros driver. Hence Crazyswarm can control more Crazyflies per Crazyradio PA, so it is still the preferred way to fly a swarm mostly when using a motion capture system. Though, with the progress made on LPS and Lighthouse positioning, running swarms, using the python API directly is a probably a more lightweight alternative.

Sneak peak of what’s coming at ICRA 2019

2019-05-13 | Tobias | 2 Comments

Only a week left until we stand in our ICRA booth in Montreal and give you a gimps of what we do here at Bitcraze. As we have been writing about earlier we are aiming to run a fully automated demo. We have been fine tuning it over the last couple of days and if something unpredictable doesn’t break it, we think it is going to be very enjoyable. For those that are interested in the juicy details check out this informative ICRA 2019 page, but if you are going to visit, maybe wait a bit so you don’t get spoiled.

Apart from the demo we are also going to show our products as well as some new things we are working on. The brand new things include:

AI-deck, Active marker deck and Lighthouse-4 deck

AI-deck: This is a collaborative product between GreenWaves Technologies, ETH Zurich and Bitcraze. It is based on the PULP-shield that the Integrated and System Laboratory has designed. You can read more about it in this blog post. The difference with the PULP-shield is that we have added a ESP32, the NINA-W102 module, so that video can be streamed over WiFi. This we hope will ease development and add more use cases.
Active marker deck: Another collaboration, but this time with Qualisys. This will make tracking with their motion capture cameras easier and better. Some more details in this blog post. Qualisys will have the booth just next to us were it will be possible to see it in a live demo!
Lighthouse-4 deck: Using the Vive lighthouse positioning system this deck adds sub-millimeter precision to the Crazyflie. This is the deck used in the demo and could become the star of the show.

Adding to the above we will of course also display our recently released products:

Crazyflie 2.1: The Crazyflie 2.1 is an improvement of the Crazyflie 2.0 but keeping backward capability.
- Better radio performance and external antenna support: With a new radio power amplifier we’ve improved the link quality and added support for dual antennas (on-board chip antenna and external antenna via u.FL connector)
- Better power button: We’ve gotten feedback that the power button breaks too easily, so now we’ve replaced with a more solid alternative.
- Improved battery cable fastening: To avoid weakening of the cables over time they are now run through a cable relief.
- Improved sensors: To make the flight performance better we’ve switched out the IMU and pressure sensor. The new Crazyflie uses the drone specialized sensor combo BMI088 and BMP388 by Bosch Sensortech.
Flow deck v2: The Flow deck v2 has been upgraded with the new ST VL53L1x which increases the range up to 4 meters
Z-ranger deck v2: The Z-ranger v2 deck has been upgraded with the new ST VL53L1x which increases the range up to 4 meters
Multi-ranger deck: The Multi-ranger deck adds VL53L1x sensors in all directions for mapping and obstacle avoidance.
MoCap marker deck: The motion capture deck with support for easily attachment of passive markers for motion capture camera tracking.
Roadrunner: The Roadrunner is released as early access and the hardware is basically a Crazyflie 2.1 without motors and up to 12V input power. This enables other robots or system to use the loco positioning system.

You can find us in booth 101 at ICRA 2019 (in Montral, Canada), May 20 – 22. Drop by and say hi, check out the products & demo and tell us what you are working on. We love to hear about all the interesting projects that are going on. See you there!

PULP-DroNet: open source and open hardware artificial intelligence for fully autonomous navigation on Crazyflie

2019-05-06 | Daniele Palossi | 3 Comments

Hi everyone, here at the Integrated and System Laboratory of the ETH Zürich, we have been working on an exciting project: PULP-DroNet.
Our vision is to enable artificial intelligence-based autonomous navigation on small size flying robots, like the Crazyflie 2.0 (CF) nano-drone.
In this post, we will give you the basic ideas to make the CF able to fly fully autonomously, relying only on onboard computational resources, that means no human operator, no ad-hoc external signals, and no remote base-station!
Our prototype can follow a street or a corridor and at the same time avoid collisions with unexpected obstacles even when flying at high speed.

PULP-DroNet is based on the Parallel Ultra Low Power (PULP) project envisioned by the ETH Zürich and the University of Bologna.
In the PULP project, we aim to develop an open-source, scalable hardware and software platform to enable energy-efficient complex computation where the available power envelope is of only a few milliwatts, such as advanced Internet-of-Things nodes, smart sensors — and of course, nano-UAVs. In particular, we address the computational demands of applications that require flexible and advanced processing of data streams generated by sensors such as cameras, which is beyond the capabilities of typical microcontrollers. The PULP project has its roots on the RISC-V instruction set architecture, an innovative academic and research open-source architecture alternative to ARM.

The first step to make the CF autonomous was the design and development of what we called the PULP-Shield, a small form factor pluggable deck for the CF, featuring two off-chip memories (Flash and RAM), a QVGA ultra-low-power grey-scale camera and the PULP GAP8 System-on-Chip (SoC). The GAP8, produced by GreenWaves Technologies, is the first commercially available embodiment of our PULP vision. This SoC features nine general purpose RISC-V-based cores organised in an on-chip microcontroller (1 core, called Fabric Ctrl) and a cluster accelerator of 8 cores, with 64 kB of local L1 memory accessible at high bandwidth from the cluster cores. The SoC also hosts 512kB of L2 memory.

Then, we selected as the algorithmic heart of our autonomous navigation engine an advanced artificial intelligence algorithm based on DroNet, a Convolutional Neural Network (CNN) that was originally developed by our friends at the Robotic and Perception Group (RPG) of the University of Zürich.
To enable the execution of DroNet on our resource-constrained system, we developed a complete methodology to map computationally-intense deep neural networks on the PULP-Shield and the GAP8 SoC.
The network outputs two pieces of information, a probability of collision and a steering angle that are translated in dynamic information used to control the drone: respectively, forward velocity and angular yaw rate. The layout of the network is the following:

Therefore, our mission was to deploy all the required computation onboard our PULP-Shield mounted on the CF, enabling fully autonomous navigation. To put the problem into perspective, in the original work by the RPG, the DroNet CNN enabled autonomous navigation of big-size drones (e.g., the Bebop Parrot). In the original use case, the computational power and memory was not a problem thanks to the streaming of images to a remote base-station, typically a laptop consuming 30-100 Watt or more. So our mission required running a similar workload within 1/1000 of the original power.
To make this work, we combined fixed-point arithmetic (instead of “traditional” floating point), some minimal modification to the original topology, and optimised memory and computation usage. This allowed us to squeeze DroNet in the ultra-small power budget available onboard. Our most energy-efficient configuration delivers 6 frames-per-second (fps) within only 64 mW (including all the electronics on the PULP-Shield), and when we push the PULP platform to its limit, we achieve an impressive 18 fps within just 3.5% of the total CF’s power envelope — the original DroNet was running at 20 fps on an Intel i7.

Do you want to check for yourself? All our hardware and software designs, including our code, schematics, datasets, and trained networks have been released and made available for everyone as open source and open hardware on Github. We look forward to other enthusiasts contributions both in hardware enhancement, as well as software (e.g., smarter networks) to create a great community of people interested in working together on smart nano-drones.
Last but not least, the piece of information you all were waiting. Yes, soon Bitcraze will allow you to enjoy of our PULP-shield, actually, even better, you will play with its evolution! Stay tuned as more information about the “code-name” AI-deck will be released in upcoming posts :-).

If you want to know more about our work:

Questions? Drop us an email (dpalossi at iis.ee.ethz.ch and fconti at iis.ee.ethz.ch)