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The beginners guide to Drones

  2024-07-22  |     |    Leave a comment

Welcome to the “The Beginner’s Guide to Drones” for programmers curious about exploring the world of unmanned aerial vehicles (UAVs). If you’re a coder from another field, this guide will walk you through the basics of drones, their components, and how to start programming them. Let’s dive in and see how your coding skills can take flight!

If you come from an engineering field, you might already know the basics of some of these topics, however you might still have use of the overview and can use the resources to get more specific knowledge.

The Robotics part

First and foremost, you’ll need some basic robotics skills.
We start of with the most basic question, “What is a Robot?”. A robot uses sensors to create an internal model of its environment, and actuators to act on/in its environment. The specifics of the internal model depend on the robot’s purpose, but a crucial component is understanding its location and orientation within that environment.

Linear algebra basics

To understand how a quadcopter perceives its environment and its own position, you’ll need some basic skills in linear algebra, particularly in matrices, vectors, and frame rotation. These skills are essential for comprehending the mathematical principles behind quadcopter navigation.

To build an internal model of its own movement and orientation, an Inertial Measurement Unit (IMU) is used. An IMU consists of a gyroscope, an accelerometer, and sometimes a magnetometer. These sensors, when combined using sensor fusion techniques (See “Control Theroy” below), help determine the quadcopter’s angular velocity and linear acceleration. This data allows the drone to calculate its orientation and movement.

Picture of a Crazyflie in relation to a reference frame from Bitcraze.io

For a detailed understanding of these processes, please refer to the following resources:
Visual description of frame transformations;
https://www.youtube.com/watch?v=kYB8IZa5AuE
Basic coordinate transformations;
https://motion.cs.illinois.edu/RoboticSystems/CoordinateTransformations.htmlbasi

Positioning techniques

The quadcopter can now determine its relationship to its starting position and the gravitational field. However, relying solely on an IMU tends to cause drift over time. Imagine trying to stand on one leg with your eyes closed—eventually, you’ll lose balance.

For improved stability, a drone often needs additional sensors, such as a camera, to help stabilize its position. Other sensor systems can also be used to determine relative or absolute position. While an IMU can sense changes in position relative to a starting point, an external positioning system is necessary for stability and obtaining absolute positions. This system acts as a reference frame for the drone.

Drones flying outdoors typically use GPS combined with RTCM, since it is available almost anywhere, ease to use, and has centimeter-level accuracy.

For indoor use, as with Crazyflies, the default used positioning system is motion-capture system but there are others as well. This area is at the cutting edge of science, with new technologies emerging constantly. However, many effective systems are available, though they may have constraints regarding power efficiency, flight area size, update speed, or precision.

This is an image of some common techniques for positioning. a) AOA, b) TOA, TWR c) TDOA taken from Researchgate.com

Control theory

Now that drone can understand its position and orientation in space, the next step is figuring out how to move within this space. Moving from point A to point B involves setting a “setpoint” and then determining how to use the drone’s actuators to reach this setpoint most efficiently. This is where control theory comes into play.

Drones generally use some sort of feedback control system, which in its most basic form looks something like this:

A very basic overview of a system with feedback control

In this system, the error (the difference between the current position and the setpoint) is fed back into the system to ensure the drone moves in a way that minimizes the error over time.

Various algorithms can calculate the best actuator output based on the error and current state. One of the most fundamental algorithms is the PID controller, which works well for linear systems. Understanding PID controllers requires some basic calculus, but the concept is straightforward. Here are some resources for simple explanations:

For IMUs, there is a particularly useful filter to know about, given its widespread use. The accelerometer and gyroscope each have their own profiles of noise and drift. The accelerometer is sensitive to short-term noise, while the gyroscope drifts slowly over time. To mitigate these issues, a combination of both measurements is often used. The complementary filter is ideal for this situation, leveraging the strengths of both sensors to correct the measurements effectively.

More information on how to use the complementary filter for IMUs can be found here:
https://www.hackster.io/hibit/complementary-filter-and-relative-orientation-with-mpu9250-d4f79d

For more complex scenarios, advanced controllers like Kalman filters and others can be used. It’s also possible to combine multiple controllers to achieve better performance.

Basic overview of feedback control systems;
https://control.com/textbook/closed-loop-control/basic-feedback-control-principles/

The flying part

Now lets get into the exciting part. FLYING!!!

Actuators

Drone actuators, primarily consisting of motors and propellers, are critical for controlling a drone’s movement and stability. The motors and propellers are typically called the “drive train” or “power train”.
The motors used on drones are usually brushed or brushless DC motors. Propellers are attached to the motors and generate lift by pushing air downwards. The size, shape, and pitch of the propellers affect the drone’s performance, including speed, lift, and maneuverability. Together, the precise control of motors and propellers enables a drone to perform complex maneuvers, maintain stability, and achieve efficient flight.

Pictures of forces acting on a drone in flight


This is an almost all you need guide to get an overview of drone flight dynamics
https://dronstechnology.com/the-physics-of-drone-flight-lift-thrust-drag-and-weight/

Stock information

As of now the Crazyflie 2.1 is out of stock, unfortunately. They’re expected back in stock around August 20th – 4 weeks from now. You can sign up in our shop to be notified as soon as they arrive!
https://store.bitcraze.io/collections/kits/products/crazyflie-2-1?variant=19575412719703

Bringing Model Predictive Control to the Crazyflie with TinyMPC

  2024-07-15  |     |    1 Comment

Today we welcome Sam Schoedel and Khai Nguyen from Carnegie Mellon University. Enjoy!

We’re excited to share the research we’ve been doing on model-predictive control (MPC) for tiny robots! Our goal was to find a way to compress an MPC solver to a size that would fit on common microcontrollers like the Crazyflie’s STM32F405 while being fast enough to control the higher frequency dynamics of smaller robots. We came up with a few tricks to make that happen and dubbed the resulting solver TinyMPC. When it came time for hardware experiments, using the Crazyflie just made sense. A tiny solver deserves a tiny robot.

Motivation

Model predictive control is a powerful tool for controlling complex systems, but it is computationally expensive and thus often limited to use cases where the robot can either carry enough computational power or when offboard computing is available. The problem becomes challenging to solve for small robots, especially when we want to perform all of the computation onboard. Smaller robots have inherently faster dynamics which require higher frequency controllers to stabilize, and because of their size they don’t have the capacity to haul around as much computational power as their larger robot counterparts. The computers they can carry are often highly memory-constrained as well. Our question was “how can we shrink the computational complexity and memory costs of MPC down to the scale of tiny robots?”

What We Did

We settled on developing a convex model predictive control solver based on the alternating direction method of multipliers. Convex MPC solvers are limited to reasoning about linear dynamics (on top of any other convex constraints), but have structure that TinyMPC exploits to solve problems efficiently. The tricks we used to achieve this efficiency are described in the paper, but it boils down to rewriting the problem as a constrained linear-quadratic regulator to reduce the memory footprint and then precomputing as many matrices as possible offline so that online calculations are less expensive. These tricks allowed us to fit long-time horizon MPC problems on the Crazyflie and solve them fast enough for real-time use.

What TinyMPC Can Do

We decided to demonstrate the constraint-handling capabilities of TinyMPC by having the Crazyflie avoid a dynamic obstacle. We achieved this by re-computing hyperplane constraints (green planes in the first video) about a spherical obstacle (transparent white ball) for each knot point in the trajectory at every time step, and then by solving the problem with the new constraints assuming they stayed fixed for the duration of the solve.

In the two videos below, the reference trajectory used by the solver is just a hover position at the origin for every time step. Also, the path the robot takes in the real world will never be exactly the same as the trajectory computed by the solver, which can easily result in collisions. To avoid this, we inflated the end of the stick (and the simulated obstacle) to act as a keep-out region.

TinyMPC is restricted to reasoning about linear dynamics because of its convex formulation. However, a simple linearization can be taken pretty far. We experimented with recovering from different starting conditions to push the limits of our linear Crazyflie model and were able to successfully recover from a 90 degree angle while obeying the thrust commands for each motor.

We recently added support for second-order cone constraints as well. These types of constraints allow TinyMPC to reason about friction and thrust cones, for example, which means it can now intelligently control quadrupeds on slippery surfaces and land rockets. To clearly demonstrate the cone constraint, we took long exposure photos of the Crazyflie tracking a cylindrical landing trajectory without any cone constraints (red) and then with a spatial cone constraint that restricts the landing maneuver to a glide slope (blue).

How To Use TinyMPC

All of the information regarding the solver can be found on our website and GitHub org (which is where you can also find the main GitHub repository). TinyMPC currently has a Python wrapper that allows for validating the solver and generating C++ code to run on a robot, and we have a few examples in C++ if you don’t want to use Python. Our website also explains how to linearize your robot and has some examples for setting up the problem with a linear model, solving it an MPC loop, and then generating and running C++ code.

Most importantly to the Crazyflie community, our TinyMPC-integrated firmware is available and should work out of the box. Let us know if you use it and run into issues!

Our accompanying research papers:

Khai Nguyen, Sam Schoedel, Anoushka Alavilli, Brian Plancher, and Zachary Manchester. “TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers.” arXiv preprint arXiv:2310.16985 (2023). https://arxiv.org/pdf/2310.16985

Sam Schoedel, Khai Nguyen, Elakhya Nedumaran, Brian Plancher, and Zachary Manchester. “Code Generation for Conic Model-Predictive Control on Microcontrollers with TinyMPC.” arXiv preprint arXiv:2403.18149 (2024). https://arxiv.org/pdf/2403.18149

We would love your feedback and suggestions, and let us know if you use TinyMPC for your tiny platforms!

Client release 2024.7 and library release 0.1.26

  2024-07-08  |     |    Leave a comment

We are happy to announce the latest updates to the Crazyflie client and Python library. Major changes include improved persistent parameter management, enhanced plotting with new x-axis manipulation features, and new default logging configurations (for PID tuning). Minor updates include bug fixes and documentation improvements.

Updated plotter tab. Besides the existing option for a number of samples, users can now set x-axis limits to a number of seconds or a time range.
Updated parameters tab. Users can now mass dump/load persistent parameters to/from a file, or clear all stored persistent parameters.

For detailed release notes, check out crazyflie-lib-python release 0.1.26 and crazyflie-clients-python release 2024.7.

Crazyflie at the Robotics Developer Day 2024

  2024-07-01  |     |    Leave a comment

Whenever we show the Crazyflie at our booth at various robotics conferences (like the recent ICRA Yokohama), we sometimes get comments like ‘ahh that’s cute’ or ‘that’s a fun toy!’. Those who have been working with it for their research know differently, but it seems that the general robotics crowd needs a little bit more… convincing! Disregarding its size, the Crazyflie is a great tool that enables users to do many awesome things in various areas of robotics, such as swarm robotics and autonomy, for both research and education.

We will be showing that off by giving a live tutorial and demonstration at the Robotics Developer Day 2024, which is organized by The Construct and will take place this Friday, 5th of July. We have a discount code for you to use if you want to get a ticket; scroll down for details. The code can be used until 12 am midnight (CEST) on the 2nd of July.

The Construct and Robotics Developer Day 2024

So a bit of background information: The Construct is an online platform that offers various courses and curriculums to teach robotics and ROS to their users. Along with that, they also organize all kinds of live training sessions and events like the Robotics Developer Day and the ROS Awards. Unfortunately, the deadline for voting in the latter has passed, but hopefully in the future, the Crazyflie might get an award of its own!

What stands out about the platform is its implementation of web-based virtual machines, called ‘ROSJects,’ where ROS and everything needed for it is already set up from the start. Anyone who has worked with ROS(2) before knows that it can be a pain to switch between different versions of ROS and Gazebo, so this feature allows users to keep those projects separate. For the ROS Developer Day, there will be about five live skill-learning sessions where a ROSject is already preconfigured and set up for the attendees, enabling them to try the tutorial simultaneously as the teacher or speaker explains the framework.

Skill learning session with the Crazyflie

One of the earlier mentioned skill learning sessions is, of course, one with the Crazyflie! The title is “ROS 2 with a Tiny Quadcopter,” and it is currently planned to be the first skill learning session of the event, scheduled at 15:15 (3:15 pm) CEST. The talk will emphasize the use of simulation in the development process with aerial robotics and iterating between the real platform and the simulated one. We will demonstrate this with a Crazyflie 2.1 equipped with a Lighthouse deck and a Multi-ranger deck. Moreover, it will also use a Qi-charging deck on a charging platform while it patiently waits for its turn :D

What we will be showing is a simple implementation of a mapping algorithm made specifically for the Crazyflie’s Multiranger deck, which we have demonstrated before at ROSCon Kyoto and in the Crazyswarm2 tutorials. What is especially different this time is that we are using Gazebo for the simulation parts, which required some skill learning on our side as we have been used to Webots over the last couple of years (see our tutorial for that). You can find the files for the simulation part in this repository, but we do advise you to follow the session first.

You can, if you want, follow along with the tutorial using a Crazyflie yourself. If you have a Crazyflie, Crazyradio, and a positioning deck (preferably Lighthouse positioning, but a Flowdeck would work as well), you can try out the real-platform part of this tutorial. You will need to install Crazyswarm2 on a separate Ubuntu machine and add a robot in your ROSject as preparation. However, this is entirely optional, and it might distract you from the cool demos we are planning to show, so perhaps you can try this as a recap after the actual skill learning session ;).

Here is a teaser of what the final stage of the tutorial will look like:

Win a lighthouse explorer bundle and a Hands-On Pass discount

We are also sponsors of the event and have agreed with The Construct to award one of the participants a Crazyflie if they win any contest. Specifically, we will be awarding a Lighthouse Explorer bundle, with a Qi deck and a custom-made charging pad similar to the ones we show at fairs like ICRA this year. So make sure to participate in the contests during the day for a chance to win this or any of the other prizes they have!

It is possible to follow the event for free, but if you’d like to participate with the ROSjects, you’ll need to get a hands-on pass. If you haven’t yet gotten a hands-on ticket for the Robotics Developer Day, please use our 50% off discount code:

19ACC2C9

This code is valid until the 2nd of July, 12 am (midnight) Central European Time! Buy your ticket on the event’s website: https://www.theconstruct.ai/robotics-developers-day/

RSS 2024 aerial swarm workshop

On a side note, we will be at the Robotics: Science and Systems Conference in Delft from July 15th to 19th, 2024—just about two weeks from now. We won’t have a booth as we usually do, but we will be co-organizing a half-day workshop titled Aerial Swarm Tools and Applications (more details on this website).

We will be organizing this workshop together with our collaborators at Crazyswarm2, as well as the developers of CrazyChoir and Aerostack2. We’re excited to showcase demos of these frameworks with a bunch of actual Crazyflies during the workshop, if the demo gods are on our side :D. We will also have great speakers, including: SiQi Zhou (TU Munich), Martin Saska (Czech Technical University), Sabine Hauert (University of Bristol), and Gábor Vásárhelyi (Collmot/Eötvös University).

Hope to see you there!

Discover the Impact of Structural Defects on Nano-Quadcopter Performance: CrazyPAD dataset

  2024-06-24  |     |    Leave a comment

This week we have a guest blogpost by Kamil Masalimov (MSc) and Tagir Muslimov (PhD) of the Ufa University of Science and Technology. Enjoy!

As researchers passionate about UAV technology, we are excited to share our recent findings on how structural defects affect the performance of nano-quadcopters. Our study, titled “CrazyPAD: A Dataset for Assessing the Impact of Structural Defects on Nano-Quadcopter Performance,” offers comprehensive insights that could greatly benefit the Crazyflie community and the broader UAV industry.

The Motivation Behind Our Research

Understanding the nuances of how structural defects impact UAV performance is crucial for advancing the design, testing, and maintenance of these devices. Even minor imperfections can lead to significant changes in flight behavior, affecting stability, power consumption, and control responsiveness. Our goal was to create a robust dataset (CrazyPAD) that documents these effects and can be used for further research and development.

Key Findings from Our Study

We conducted a series of experiments by introducing various defects, such as added weights and propeller cuts (Figure 1), to nano-quadcopters. For the experiments, we used the Lighthouse Positioning System with two SteamVR 2.0 virtual reality stations (Figure 2).

Figure 1. Propeller with two side defects
Figure 2. Schematic of the experimental setup with Lighthouse Positioning System

Here are some of the pivotal findings from our research:

  1. Stability Impact: We observed that both added weights and propeller cuts lead to noticeable changes in the stability of the quadcopter. Larger defects caused greater instability, emphasizing the importance of precise manufacturing and regular maintenance.
  2. Increased Power Consumption: Our experiments showed that structural defects result in higher power consumption. This insight is vital for optimizing battery life and enhancing energy efficiency during flights.
  3. Variable Control Responsiveness: We used the standard deviation of thrust commands as a measure of control responsiveness. The results indicated that defects increased the variability of control inputs, which could affect maneuverability and flight precision.
  4. Changes in Roll and Pitch Rates: The study also highlighted variations in roll and pitch rates due to structural defects, providing a deeper understanding of how these imperfections impact flight dynamics.

We show Figure 3 as an example of a graph obtained from our dataset. In this figure, you can see the altitude and thrust command over time for different flight conditions. The blue line represents the normal flight, while the orange line represents the flight with additional weight near the M3 propeller. In Figure 4, you can see the 3D flight trajectory of the Crazyflie 2.1 quadcopter under the cut_propeller_M3_2mm condition with the corrected ideal path. The blue line represents the actual flight trajectory, while the red dashed line with markers represents the ideal trajectory. Figure 5 shows the Motor PWM values over time for the add_weight_W1_near_M3 condition. The plot shows the PWM values of each motor (M1, M2, M3, and M4) as they respond to the added weight near the M3 propeller.

More examples of graphs obtained from the CrazyPAD dataset can be found in our research paper specifically describing this dataset: https://doi.org/10.3390/data9060079

Figure 3. Altitude and thrust command over time for different flight conditions
Figure 4. 3D flight trajectory of the Crazyflie 2.1
Figure 5. Motor PWM values over time

Leveraging Research for Diagnostic and Predictive Models

One of the most exciting aspects of our research is its potential application in developing diagnostic and predictive models. The CrazyPAD dataset can be utilized to train machine learning algorithms that detect and predict structural defects in real-time. By analyzing flight data, these models can identify early signs of wear and tear, allowing for proactive maintenance and reducing the risk of in-flight failures.

Diagnostic models can continuously monitor the performance of a UAV, identifying anomalies and pinpointing potential defects. This real-time monitoring can significantly enhance the reliability and safety of UAV operations.

Predictive models can forecast future defects based on historical flight data. By anticipating when and where defects are likely to occur, these models can inform maintenance schedules, ensuring UAVs are serviced before issues become critical.

Why This Matters for the Crazyflie Community

The CrazyPAD dataset and our findings offer valuable resources for the Crazyflie community. By understanding how different defects affect flight performance, developers and enthusiasts can improve design protocols, enhance testing procedures, and ensure higher safety and performance standards for their UAVs.

We believe that sharing our research with the Crazyflie community can lead to significant advancements in UAV technology. The dataset we created is open under the MIT License for further exploration and can serve as a foundation for new innovations and improvements.

Get Involved and Explore Further

We invite community members to explore our full research article and the CrazyPAD dataset. Together, we can drive forward the standards of UAV technology, ensuring that Crazyflie remains at the forefront of innovation and excellence.

Our research paper with a detailed description of this dataset:

Masalimov, K.; Muslimov, T.; Kozlov, E.; Munasypov, R. CrazyPAD: A Dataset for Assessing the Impact of Structural Defects on Nano-Quadcopter Performance. Data 2024, 9, 79. https://doi.org/10.3390/data9060079

Dataset:  https://github.com/AerialRoboticsUUST/CrazyPAD

We are eager to collaborate with the Crazyflie community and welcome any feedback or questions regarding our research. Let’s work together to push the boundaries of what’s possible in UAV technology.

SWEDISH SUMMER, 2024 edition

  2024-06-17  |     |    Leave a comment

If you know a little about Sweden, you know about Midsommar. Nothing like the 2019 horror film (thankfully), though we do wear flower crowns and dance around the pole. It’s usually an occasion to celebrate with family (and alcohol) those long days and short nights. We actually celebrated a little bit early today with a nice team barbecue.

Midsommar is next Friday, and it marks the official kickoff to summer here in Sweden. That also means that for the coming 2 months, Bitcraze will be operating with a slightly smaller team. So, what does that mean for you? This should not impact you too much, of course, but here are the most important things to note:

  • Developer Meetings: First up, our regular developer meetings will be on hiatus during July and August. It’s a great time for everyone, including you, our regular attendees, to take a breather. We’ll be back in action come September, resuming our usual schedule of one meeting per month.
  • GitHub: With fewer team members around, our response time on GitHub discussions might be a bit slower. We strive to answer every question, but with less manpower, some issues might only get resolved when the person with the right expertise returns from their vacation. We appreciate your patience and understanding during this time.
  • Orders: Shipping and deliveries should have the same pace, so don’t worry about your order coming on time, we’ve got that covered!

Ongoing projects and maintenance

Our focus during the summer will shift primarily to maintenance and continuing work on ongoing projects. We’re putting the final touches on the Brushless project of course, and will keep working on its charger solution too.

We’re excited to announce our participation in Robotics Developers Day on July 5th. We’re not only sponsors of this event, we’re also thrilled that Kimberly will be hosting a live learning session about ROS 2 on the Crazyflie.

For us at Bitcraze, summer is a wonderful opportunity to slow down a bit and focus on projects we don’t usually have time for. This could mean diving deeper into learning RUST, reorganizing the office, or trying some prototypes out. It’s a chance to reset and prepare for the busy months ahead.

We hope you have a fantastic summer filled with sunshine, relaxation, and lots of new adventures. Glad Midsommar, as we say here!

ICRA 2024 POSTERS

  2024-06-10  |     |    Leave a comment

As we mentioned earlier, ICRA Yokohama was full of exciting encounters – we really enjoyed meeting researchers, tech companies, and enthusiastic roboticists during those 4 days.

One challenge was to bring back as many research posters featuring the Crazyflies as possible. The goal was to decorate the walls of the office with them, as a “hall of fame”. And I’m really, really proud to show you how it turned up!

This was before
And this is now!

In total, we received 6 new posters. Here they are:

Optimal Collaborative Transportation for Under-Capacitated Vehicle Routing Problems using Aerial Drone swarms
Akash Kopparam Sreedhara, Deepesh Padala, Shashank Mahesh, Kai Cui, Mengguang Li, Heinz Koeppl

This paper presents a strategy for optimizing the collaborative transportation of payloads in an under-capacitated vehicle routing scenario. The Crazyflies work together to dynamically adjust routes based on real-time data and transport capacities, and collaborate to lift and transport heavier payloads.

From Shadows to Light: A Swarm Robotics Approach With Onboard Control for Seeking Dynamic Sources in Constrained Environments
T. A. Karagüzel, V. Retamal, N. Cambier and E. Ferrante

The paper describes a method for enabling a swarm of Crazyflies to dynamically seek and locate a moving target or source in constrained, GNSS-denied environments. Using a simple rule-based approach, the drones track dynamic source gradients and navigate obstacles autonomously with fully onboard systems.

CrazySim: A Software-in-the-Loop Simulator for the Crazyflie Nano Quadrotor
Christian Llanes, Zahi Kakish, Kyle Williams, and Samuel Coogan

We actually already have a blogpost presenting this paper, and we’re so happy to have it represented in our office now!

TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers
Khai Nguyen, Sam Schoedel, Anoushka Alavilli, Brian Plancher, Zachary Manchester

The paper presents TinyMPC, a high-speed model-predictive control (MPC) solver designed for resource-constrained microcontrollers on small robots like the Crazyflie. TinyMPC efficiently handles real-time trajectory tracking and dynamic obstacle avoidance, outperforming traditional solvers.

Robust and Efficient Depth-Based Obstacle Avoidance for Autonomous Miniaturized UAVs
H. Müller, V. Niculescu, T. Polonelli, M. Magno and L. Benini

The paper introduces a lightweight obstacle avoidance system for nano quadcopters, leveraging a novel 64-pixel multizone time-of-flight (ToF) sensor to safely and effectively navigate complex indoor environments. Tested on the Crazyflie 2.1, the system achieves 100% reliability at a speed of 0.5 m/s, all while using only 0.3% of the onboard processing power, demonstrating its suitability for autonomous operations in unexplored settings.

Fully onboard Low-power localization with semantic sensor fusion on a Nano-UAV using floor plans
Nicky Zimmerman, Hanna Müller, Michele Magno, Luca Benini

This paper introduces a method for autonomous localization in nano-sized UAVs like the Crazyflie by fusing geometric data from time-of-flight sensors with semantic information extracted from images. The approach leverages annotated floor plans to improve navigation accuracy without adding extra deployment costs. The system operates efficiently with limited onboard computational resources, achieving a 90% success rate in real-world office environments.

A big thanks, once again, to all of those who gave us their posters!

The Aerial Robotic Landscape

  2024-06-03  |     |    Leave a comment

It’s been over a little year since we started the ROS Aerial Robotics community group together with the Drone Code Foundation, and it is still going strong (blogpost 1, blogpost 2). Since there is a nice mix of people joining the meetings from different backgrounds and drone operating systems, we have had quite a few discussions and overviews of various topics. For instance, we’ve explored courses in Aerial Robotics and other subjects in previous meetings. An important goal of the group has been to make it easier for people to get started with flying robotics, which we’ve achieved by collecting essential information in the ‘Aerial Robotic Landscape’.

Starting out in Aerial Robotics

Let’s cut to the chase: Aerial Robotics is a very challenging field to get started in. Not only do you need a comprehensive understanding of which hardware to acquire, but users also face a multiple choices. These decisions include selecting the right autopilot, simulator for testing ideas, and necessary sensors to achieve autonomy. Unlike the well-established Turtlebot in other robotics domains, there isn’t a universally accepted and field-tested getting-started development drone in the aerial robotics world. While we at Bitcraze would love everyone to go for the Crazyflie, we recognize its limitations. Like, it may not handle outdoor flights with GPS or carry heavy cameras effectively. Our goal, as the ‘Aerial Robotics Community group,’ is to make it easier for beginners by providing users with information about the hardware and software they truly need.

Drone Code Foundation and Bitcraze AB had a keynote speech together at ROSCon 2023 about getting started in Aerial Robotics called ‘Up, Up, and Away: Adventures in Aerial Robotics’. Please take a look at the talk here on Vimeo.

The Aerial Robotics Landscape website

The Aerial Robotics Landscape serves as a repository of information related to all things Aerial Robotics. It started out in the GitHub repository, and it grew due to the discussions held at the aerial robotics community group meetings. Additionally, contributions from both group members and external contributors have played an important role (you can explore the merged PRs).

As the pages and tables expanded, it became clear that a better representation was necessary than just the mere README documentation on the GitHub repository. The group therefore experimented with MKDocs, creating a website in the ‘Read the Docs’ theme. This is a similar theme that important packages within in the ROS ecosystem use, such as the ROS documentation, as well as ROS 2 packages like Nav2 and Crazyswarm2.

Please take a look at the rendered website here: https://ros-aerial.github.io/aerial_robotic_landscape/

Please contribute!

The Aerial Robotics Landscape is a dynamic , where development kits emerge while others are discontinued, new simulators rise while some remain unsupported, and autopilot and autonomy features evolve monthly. This ever-changing landscape demands constant updates and additions. We try to do this to the best of our ability, but we can’t do it alone — we need your help.

If you believe that your favorite hardware platform is missing from the landscape, or if you’ve recently developed a new planning algorithm for fixed-wing vehicles or created a YouTube course on optical-based flight, please contribute by means of a pull request to the GitHub repository. We’ve put together a guide on how to contribute to the Aerial Robotics Landscape here. Let’s make the website useful together!

If you’d like to join the ROS aerial Robotics meetings, please take a look at our community github repository for joining information. The next meeting is the 5th of June, 4 PM UTC and was announced on ROS discourse.

Back from ICRA 2024

  2024-05-27  |     |    Leave a comment

Two weeks ago, Arnaud, Barbara and Rik were at ICRA 2024 in Yokohama. At our booth we showed our current products as well as the upcoming brushless Crazyflie and the camera deck prototype.

As usual, the conference has been very busy with a lot of visitors and a lot of very interesting discussion. Thanks to everyone that passed by the booth, we have come back to Sweden with a lot of energy and new ideas!

The autonomous lighthouse swarm demo demo has worked pretty well. If you are interested to know more about it you can visit our event page. It is an autonomous demo with 3 brushless Crazyflies and 6 Crazyflie 2.1s flying autonomously. With the extended battery life of the brushless, we could operate the demo pretty much continuously.

If you’ve been at the conference, you may have spotted someone proudly sporting our exclusive ‘Bitcraze took my poster’ button. We’re excited to have received posters covering a wide range of topics, the walls of our office are eagerly awaiting these visual representations of your hard work and dedication. Thank you to everyone who has contributed.

The Crazyflie 2.1 brushless is getting optional propeller guards

  2024-05-21  |     |    3 Comments

One of the great features of the stock Crazyflie 2.1 is that it is more or less harmless. The Crazyflie 2.1 brushless weighs roughly the same but has almost twice the amount of thrust force, so a little bit of more care is needed. We therefore decided to provide optional propeller guards. While propeller guards adds safety they also add weight and disrupt the air flow from the propellers. Adding to that, the weight is located far from the center which increases the inertia even further, resulting in a less agile drone. For some applications this is not a problem but for others it is, this is why we are making them optional, meaning they are easy to replace with simple landing legs by utilizing a snap-on fitting.

The design is not fully finalized yet but we are getting close, voilá!

If the design goes according to plan they will also withstand some bumping against walls which will be a very nice feature for many applications.

Further the landing legs and propeller guards are designed in a way so they will detach during high force impacts to prevent the PCB arms from breaking.