Category: Loco Positioning

Accurate indoor localization is a crucial enabling technology for many robotic applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) localization technology, in particular, has been shown to provide robust, high-resolution, and obstacle-penetrating ranging measurements. Nonetheless, UWB measurements are still corrupted by non-line-of-sight (NLOS) communication and spatially-varying biases due to doughnut-shaped antenna radiation pattern. In our recent work, we present a lightweight, two-step measurement correction method to improve the performance of both TWR and TDoA-based UWB localization.  We integrate our method into the Extended Kalman Filter (EKF) onboard a Crazyflie and demonstrate a closed-loop position estimation performance with ~20cm root-mean-square (RMS) error.

A stylized depiction of our UWB indoor localization system and the schematics of the proposed estimation framework.

Methodology

UWB measurement errors can be separated into two groups: (1) systematic bias caused by limitations in the UWB antenna pattern and (2) spurious measurements due to NLOS and multi-path propagation. We propose a two-step UWB bias correction approach exploiting machine learning (to address(1)) and statistical testing (to address (2)). The data-driven nature of our approach makes it agnostic to the origin of the measurement errors it corrects. 

(1) Neural Network Bias Correction

The doughnut-shaped antenna radiation pattern causes the relative poses of anchors and tags to have a noticeable impact on the received signal power, which leads to systematic, predictable biases.  To empirically demonstrate the systematic measurement errors resulting from varying the relative pose between anchors and tags, we placed two DWM1000 UWB anchors at a distance of 4m and collected both TWR and TDoA UWB range measurements for the UWB tag mounted on top of a Crazyflie spinning around its own z-axis.

Left: schematics of the ranges (∆p’s), azimuth (α’s) and elevation angles (β’s) defining the relative poses of tag T and anchors A0, A1 when collecting the systematic bias measurements. Right: the neural network’s inferred bias (in red) with respect to the tag’s varying azimuth angle towards anchor T0, αT0, plotted against the UWB raw measurements.

We choose to leverage the nonlinear representation power of neural networks to learn the systematic bias which only depends on anchor-tag relative poses. Considering the limited onboard computation power, we select a fully connected neural network with 50 neurons in each of two layers with ReLU activation. To represent the relative pose between the UWB tag and anchors, we select the relative distance ∆p and roll, pitch, and yaw angles of the quadcopter as the input features x for the network. As we used fixed anchors, we do not include their poses as inputs (this level of generalization is left for future work). Given sufficient training data, the spatially-varying measurement bias can be described by a nonlinear function b=f(x) captured by the trained neural network.

(2) Outlier (Spurious Measurements) Rejection

Besides our learning-based bias correction, we use a quadcopter’s dynamic model to filter inconsistent UWB range measurements. Given the estimated velocity v and maximum acceleration amax, we can compute the maximum distance dmax a quadcopter can cover during time ∆t. Based on this information, we can reject unattainable measurements before fusing them into the EKF by comparing the measurement innovation with dmax

Moreover, we use a statistical hypothesis test to further classify potential outlier measurements. Since the measurement innovation vector is assumed to be distributed according to a multivariate Gaussian distribution, the normalized sum of squares of its values should follow a Chi-square distribution. We use the Chi-square hypothesis test to determine whether a measurement innovation is likely coming from this distribution.

UWB measurement bias f (x) prediction performance of the trained neural network (in red) compared to the actual measurement errors (blue dots) as well as the role of model-based filtering (purple dots) and statistical validation (orange dots) in rejecting outlier measurement innovations (teal dots) during a 60” flight experiment.

Data Collection and Training

We use a Crazyflie 2.0 quadcopter and the Loco Positioning System (LPS)’s UWB DW1000 modules as our research platforms. Our calibration approach runs on the Crazyflie STM32 microcontroller within the FreeRTOS real-time operating system. We equipped a cuboid flying arena with 8 UWB anchors, one for each vertex. The anchor positions were measured using a Leica total station theodolite.

Left: three-dimensional plot of our flight arena showing the positions and poses of the eight UWB DW1000 anchors (each facing towards its own x-axis, i.e., the red versor). Right: two of the training trajectories we flew to collect the samples that we used to train our neural network-based bias estimator

For all experiments, the ground truth position of the Crazyflie was provided by 10 Vicon cameras. The neural network was trained using PyTorch. To perform inference on the Crazyflie’s microcontroller, we re-use PyTorch’s trained weights in a plain C re-implementation. Since the DW1000 modules in the LPS provide UWB measurements every 5ms, the neural network inference runs at 200Hz during flight as well. Our outlier rejection method is also implemented in plain C and merged with the onboard EKF.

Close-loop Position Estimation Performance

We demonstrate the position estimation and close-loop performance of the proposed methods by flying a Crazyflie quadcopter along planar and non-planar circular trajectories (which were not among the trajectories used for training). A comparison between the estimation error of (A) the UWB localization estimate enhanced with outlier rejections and (B) the estimated enhanced with both outlier rejection and neural network bias compensation is conducted in our experiments for both TWR and TDoA2 modes. We repeated all of our experiments 10 times with a target velocity of 0.375m/s. The quadcopter trajectories during these flight tests are displayed in the following plots.  

Flight paths and the tracking performance of our approach with (in blue) and without (in orange) the neural network bias correction for two reference trajectories (planar and non-planar circular orbits) and both UWB modes (TWR and TDoA).

The distributions of the RMS estimation errors are summarized into a box plot. TWR-based ranging results in better localization performance than TDoA. However, we observe that, with our neural network bias compensation, the average RMS error of TDoA localization is around 0.21m, which is comparable to that of TWR-based localization (~0.19m). Thanks to the neural network bias compensation, the average reduction in the RMS error is ~18.5% and 48% for TWR and TDoA, respectively. Most notably, this result suggests that bias compensation might help closing the performance gap between TWR- and TDoA-based localization.

Root mean square error (RMSE) of the quadcopter position estimate before (in orange) and after (in blue) the neural network calibration step for both TWR and TDoA ranging modes. Each pair of box plots refers to a planar reference trajectory (left of each pair) and a reference trajectory with varying z (right of each pair), showing a greater performance enhancement for the latter.

Outlook

In this work, we presented a two-step methodology to improve UWB localization—for both TWR- and TDoA-based measurements. We used a lightweight neural network to model and compensate for pose-dependent and spatially-varying biases and an outlier rejection mechanism to filter spurious measurements. Through several real world flight experiments tracking different trajectories, we showed that we are able to improve localization accuracy for both TWR and TDoA, granting safer indoor flight. In our future work, we will include the anchors’ pose information to allow our method to further generalize to previously unobserved indoor environments, with different anchor configurations.

Links

The authors are with the Dynamic Systems Lab, Institute for Aerospace Studies, University of Toronto, Canada, and affiliated with the Vector Institute for Artificial Intelligence in Toronto.

Feel free to contact us if you have any questions or ideas: wenda.zhao@robotics.utias.utoronto.ca. Please cite this as:

<code>@article{wenda2020learning,
  title={Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots},
  author={Wenda Zhao and Abhishek Goudar and Jacopo Panerati and Angela P. Schoellig},
  journal={arXiv preprint arXiv:2003.09371},
  year={2020}
}</code>

We have been traveling a lot this Autumn and have been talking to many Crazyflie users. It is always great to talk to our users and to get feedback about how what we make is being used.

The Swarm bundle

One particular subject that stood out was the Loco positioning system (LPS): the LSP seems to be used by a lot of people and we have gotten quite some feedback about it, some are about things that work but also some things that could be improved. This is interesting because we normally do not get that much feedback from people using the LPS.

We released the LPS about 2.5 years ago with Two-Way-Ranging single Crazyflie support, and it has been improved regularly since then among other things by adding 2 TDoA modes that supports multiple Crazyflies as well as releasing the Roadrunner board, a standalone LPS tag.

If you are using the LPS, it would be great to have some feedback about what you are using it for, what works, what does not work and (even better ;) if you have any improvement that can be pushed to the community. Do not hesitate to post in this blog post, on the forum or by posting issues or pull request in the LPS-node or Crazyflie-firmware github projects.

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!

As we have written about before we moved to a new office last month. One of the major reasons was the need for a bigger flight lab. This will enable us to do better testing and improve how we can develop things, especially our positioning solutions. Even though it is not a huge place, going from 4x4x3m to 8x8x3.5m and possibly 13x9x3.5m, is a great improvement for us. We call this great playground for the Arena as this is what it feels like for us :-). Very soon we hope to have our Qualisys MoCap system, Lighthouse and the Loco positioning running, maybe even at the same time.

The Arena

As a bonus this is a great pace to play HTC Vive VR games, we just need to get the wireless transmitter so we can make full use of the space!

One of the first flights with LPS Two Way Ranging

This week we have a guest blog post from Javier Burgués. Enjoy!

I would like to introduce you a rather unknown application of the CrazyFlie 2.0 (CF2): chemical sensing. Due to its small form-factor, the CF2 is an ideal platform for carrying out gas sensing missions in hazardous environments inaccessible to terrestrial robots and bigger drones. For example, searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion.

To evaluate the suitability of the CF2 for these tasks, I developed a custom deck, named the MOX deck, to interface two metal oxide semiconductor (MOX) gas sensors to the CF2. Then, I performed experiments in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. From the measurements collected in motion (i.e. without stopping) along a predefined 3D sweeping path that takes around 3 minutes, the CF2 builds a map of the gas distribution and identifies the most likely source location with high accuracy.

1. MOX deck

The MOX deck (Fig. 1a) contains two sockets for 4-pin Taguchi-type (TGS) gas sensors, a temperature/humidity sensor (SHT25, Sensirion AG), a dual-channel digital potentiometer (AD5242BRUZ1M, Analog Devices, and two MOSFET p-type transistors (NX2301P, NEXPERIA). I used TGS 8100 sensors (Figaro Engineering) due to its compatibility with 3.0 V logic, power consumption of only 15 mW (the lowest in the market as of June 2016) and miniaturized form factor (MEMS). Since the sensor heater uses 1.8V, two transistors (one per sensor) reduce the applied power by means of pulse width modulation (PWM). The MOX read-out circuit (Fig. 1b) is a voltage divider connected to the μC’s analog-to-digital converter (ADC). The voltage divider is powered at 3.0 V and the load resistor (RL) can be set dynamically by the potentiometer (from 60 Ω to 1 MΩ in steps of 3.9 kΩ). Dynamic configuration of the load resistor is important in MOX gas sensors due to the large dynamic range of the sensor resistance (several orders of magnitude) when exposed to different gas concentrations. The sensors were calibrated (by exposing them to several known concentrations) to convert the raw output into parts-per-million (ppm) concentration units.

The initialization task of the deck driver configures the PWM, initializes the SHT25 sensor, sets the wiper position of both channels of the potentiometer and adds the MOX readout registers to the list of variables that are continuously logged and transmitted to the base station. The main task of the deck driver reads the MOX sensor output voltage and the temperature/humidity values from the SHT25 and sends them to the ground station at 10 Hz.

2. Experimental Arena, External Localization System and Gas Source

Experiments were performed in a large robotics laboratory (160 m2 × 2.7 m height) at Örebro University (Sweden). The laboratory is divided into three connected areas (R1–R3) of 132 m2 and a contiguous room (R4) of 28 m2 (Fig. 2). To obtain the 3D position of the drone, I used the Loco positioning system (LPS) from Bitcraze, based on ultra-wide band (UWB) radio transmitters. Six LPS anchors were positioned in known locations of the experimental arena and one LPS tag was fixed to the drone. The six LPS anchors were placed in the central area of the laboratory, shaped in two inverted triangles (below and above the flight area).

A gas leak was emulated by placing a small beaker filled with 200 mL of ethanol 96% in different locations of the arena (Fig. 4). Ethanol was used because it is non-toxic and easily detectable by MOX sensors. Two experiments were carried out to check the viability of the proposed system for gas source localization and mapping in complex environments. In the first experiment, the gas source was placed on top of a table (height = 1 m) in the small room (R4). In the second experiment, the source was placed inside the suspended ceiling (height = 2.7 m) near the entrance to the lab (R1). Since the piping system of the lab runs through the suspended ceiling, the gas source could represent a leak in one of the pipes. A 12 V DC fan (Model: AD0612HB-A70GL, ADDA Corp., Taiwan) was placed behind the beaker to facilitate dispersion of the chemicals in the environment, creating a plume. The experiments started five minutes after setting up the source and turning on the DC fan.

3. Navigation strategy

The drone was sent to fly along a predefined sweeping path consisting of two 2D rectangular sweepings at different heights (0.9 m and 1.8 m), collecting measurements in motion (Fig. 5). These two heights divide the vertical space of the lab in three parts of equal size. Flying first at a lower altitude minimizes the impact of the propellers’ downwash in the gas distribution. For safety reasons, the trajectory was designed to ensure enough clearance around obstacles and walls, and people working inside the laboratory were told to remain in their seats during the experiments. The ground station communicates the flight path to the drone as a sequence of (x,y,z) waypoints, with a target flight speed of 1.0 m/s. The CF2 reports the measured concentration and its location to the ground station every 100 ms.

At the end of the exploration, the ground station uses all the received information to compute a 3D map of the instantaneous concentration and the ’bouts’. A ’bout’ is declared when the derivative of the sensor response exceeds a certain threshold. Bouts are produced by contact with individual gas patches and some authors use them instead of the instantaneous response (which is more affected by the slow response time of chemical sensors). For gas source localization, we compare two approaches: using the cell with maximum value in the concentration map or using the cell with maximum bout frequency. The bout frequency (bouts/min) is computed as the bout count in a 5 second sliding window multiplied by 12 (to convert it to bouts/min).

4. Results

In the first experiment, the drone took off near the entrance of the lab (R1), 17 meters downwind of a gas source located in the other end of the laboratory (R4). From the gas distribution map (Fig. 6a) it is evident that the gas source must be in R4, because the maximum concentration (35 ppm) was found there while concentrations below 5 ppm were measured in the rest of the lab. The gas plume can be outlined from the location of odor hits. The highest odor hit density (25 hits/min) was found also in R4. The cells corresponding to the maximum concentration (green start) and maximum odor hit frequency (blue triangle) were found at 0.94 and 1.16 m of the true source location, respectively.

In the second experiment, the gas source was located just above the starting point of the exploration, hidden in the suspended ceiling (Fig. 7). The resulting maximum concentration in the test room was measured when the drone flew at h=1.8 m, highlighting the importance of sampling in 3D for localization and mapping of elevated gas sources. However, since the source is presumably not directly exposed to the environment, concentrations below 3 ppm were found in most locations of the room, which complicates the gas source localization task. The concentration and odor hit maps suggest that the gas source is located in the division between R1 and R2, which represents a localization error of 4.0 and 3.31 m, respectively.

5. Conclusions

These results suggest that the CF2 can be used for gas source localization and mapping in large indoor environments. In contrast to previous works in which long measurement times were taken at predefined or adaptively chosen sampling locations, a rough approximation of such maps can be obtained in very short time with concentration measurements acquired in motion. The obtained gas distribution maps seem coherent with respect to the true source location and wind direction, and not only enable the detection of the source with relatively small localization errors but also provide a rich visual interpretation of the gas distribution.

If you are interested in more details about this work, take a look at the journal paper or drop me an email at <jburgues8 at gmail dot com> or leave a comment on the blog!

We are glad to announce that we have manufactured the fist batch of Lightouse positioning decks and hopefully it will be ready to ship by the end of the month!

The Lighthouse positioning deck is a Crazyflie 2 deck capable of receiving IR signals from HTC Vive tracking base station (ie. Lighthouses). The basestations works by spinning IR laser beams that are received by the deck to measure the angle at which the base station sees the receiver. This allows the Crazyflie to estimate its position with great accuracy and so to fly autonomously.

The board we produced is very similar architecture-wise to the prototype we showed in previous blog posts. The main physical difference is that we now only have horizontal receivers. This change was made because we do not yet have a satisfactory mechanical solution to mount vertical IR receivers and we arbitrated that horizontal-only sensor already provides great performance for autonomous flight. Functionally it means that the Crazyflie should fly bellow the base stations to be able to position itself, we found that flying ~40cm bellow the base station gave good flying performance. We will continue looking at solution to make a deck with more receiver to increase the flight space in the future.

The lighthouse deck acquires the IR pulses transmitted by the lighthouses, the Crazyflie can then interpret these pulses to estimate its position. We also added soldering pads for a 2.54mm pin header which would allow to interface other microcontroller boards to the deck:

Lighthouse deck architecture

HTC has released 2 versions of the base stations that are incompatible with each other. Version 1 supports 2 base stations per system, and version 2 can support more than 2. We have good initial support for version 1 both in the deck and in the Crazyflie. Version 2 is currently being worked-on but early work shows that the deck should be compatible with version 2 with only a firmware update.

This leads to the current state of the product. The boards have been manufactured and we have received them but they are currently programmed with a test firmware. As previously stated the basic functionality is there but we still don’t have any finished bootloader. As soon as this is finished and tested we will start flashing all the boards. After that is is just a matter of adding them to the web-store stock and they will be ready to ship!

For now we consider this deck as early access, which means that we will document it in the wiki and that the software will still be heavily developed. For example an early limitation that will be worked-on is that it is currently required to run SteamVR on a computer to setup the system, this means that you need to have a full Vive VR setup or at least a vive gamepad or tracker to setup your flight space. Eventually we want to make it possible to setup the system with only base stations and a Crazyflie, without using steamVR.

We have added the deck to our web store so that you can subscribe to get notified as soon as it is in stock, we will of course post on the blog with more informations when this happens. In the mean time we can share again the video we did for the holidays that was made with 3 Crazyflie 2.1 equipped with the lighthouse deck using 2 V1 base stations:

We are happy to announce that the Roadrunner soon will be available in our store. The Roadrunner is an Ultra Wide Band (UWB) tag that can be used to acquire the position of any robot or object in a Loco Positioning System, which makes the LPS work with more than the Crazyflie.

The Roadrunner

The Roadrunner started out as a joint project with a customer that wanted to track go-karts on a track, but we think it should be equally useful for tracking any robot or vehicle indoors. It is essentially a Crazyflie 2.1 with an integrated LPS deck, but stripped of all quadcopter stuff, all in a nice package. It can be interfaced through the Crazyradio and USB, but also through a UART if needed. It can be powered with anything between 4 – 17V. Since it is based on the Crazyflie 2.1 platform, all tools, libraries and clients are compatible. It also has the same expansion port which makes it compatible with existing decks and can be extended with custom hardware.

You might be curious about the name we choose? We usually name internal projects after birds and what could be a better name for tracking a car than the Roadrunner? We liked the name and decided to stick to it when releasing it as a product.

We release the Roadrunner as an Early access product since we are a bit uncertain of how it will be used. We hope to get feedback from anyone using it and improve the design if needed.

This is also the first product to be released based on our new platform concept. We will release a number of new hardware designs in the near future and the platform concept is intended to simplify managing and building firmware binaries for the different hardware configurations.

On a side note, Arnaud from Bitcraze and Fred, the maintainer of the Crazyflie android client, will be visiting FOSDEM 2019 in Brussels at the end of the month. If you want to meet us there just ping us in the comment, by mail, on twitter or on the forum.

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.

Last week we have been focusing on making a release for nearly all our firmware and software. This was done mainly to support the new products we will release this fall but it also contains a lot of other functionality that have been added since the previous release. In this blog-post we will describe the most important features of this release.

New Loco Positioning status and configuration tab

New deck support

The Crazyflie firmware and Crazyflie client 2018.10 adds support for a range of new decks that are about to be released:

  • Flow deck V2 and Z-Ranger V2: New versions of the flow and Z-Ranger deck that uses the new VL53L1 distance sensor. Drivers are implemented in the Crazyflie firmware and the client has been updated to allow flying up to 2 meter in height hold and hover modes when the new decks are detected.
  • Multiranger deck: Diver for the new Multiranger deck is implemented in the Crazyflie firmware, support code is now present in the lib as well as an example implementing the push demo that makes the Crazyflie fly in hover mode using the flow deck and move away from obstacles:

The Flow deck V2 is already available in our webstore. The Z-Ranger V2 and Multiranger will be available in the following weeks, stay tuned on the blog for updated information.

Crazyswarm support

During the year, functionality implemented for the Crazyswarm project has been merged back to the Crazyflie firmware master branch. Practically it means that the Crazyflie firmware 2018.10 is the first stable version to support Crazyswarm. The main features implemented by Crazyswarm are:

  • Modular controller and estimator framework that allows to switch the estimator or the controller at runtime. Practically it means that it is not required to recompile the firmware to use a different controller anymore.
  • Addition of a high-level commander that is able to generate setpoints for the controller from within the Crazyflie. The high-level commander is usable both from Crazyswarm and from the Crazyflie python library. It currently has commands to take-off, land, go to a setpoint and follow a polynomial trajectory. It is made in such a way that it can be extended in the future.
  • Addition of the Mellinger controller: a new controller that allows to fly much tighter and precise trajectories than the PID controller. It is tuned pretty tight so it is currently mostly usable using a motion capture or lighthouse as positioning and togeather with the high-level commander.

Improved and more stable Loco Positioning System

A lot of work has been put in the Loco Positioning System (LPS) this summer. The result of this work is the creation of a new ranging mode: TDoA3. TDoA3 allows to fly as many Crazyflie as we want in the system and to add as many anchors are needed, see our previous blog-post for more information. With this release TDoA 3 is added as a stable ranging mode for LPS. The added features related to LPS are:

  • Added TDoA3 as a ranging mode in the LPS-Node-firmware, the Crazyflie 2.0 firmware and the Crazyflie client
  • Revampted the Crazyflie client LPS tab and communication protocol to handle more than 8 anchors
  • Implementation of a new outlier detector for TDoA2 and TDoA3 that drastically improve positioning noise and flight quality

Release notes and downloads

As usual the release build and release note is available on Github. The Crazyflie client and lib are also available as python pip package as cfclient and cflib.