Robohub.org
 

Quadrotor automatically recovers from failure or aggressive launch, without GPS

Credit: Robotics & Perception Group, University of Zurich.

Photo credit: Robotics & Perception Group, University of Zurich.

When a drone flies close to a building, it can temporarily lose its GPS signal and position information, possibly leading to a crash. To ensure safety, a fall-back system is needed to help the quadrotor regain stable flight as soon as possible. We developed a new technology that allows a quadrotor to automatically recover and stabilize from any initial condition without relying on external infrastructure like GPS. The technology allows the quadrotor system to be used safely both indoors and out, to recover stable flight after a GPS loss or system failure. And because the recovery is so quick, it even works to recover flight after an aggressive throw, allowing you to launch a quadrotor simply by tossing it in the air like a baseball.

How it works

Photo credit: Robotics & Perception Group, University of Zurich.

Photo credit: Robotics & Perception Group, University of Zurich.

Our quadrotor is equipped with a single camera, an inertial measurement unit, and a distance sensor (Teraranger One). The stabilization system of the quadrotor emulates the visual system and the sense of balance within humans. As soon as a toss or a failure situation is detected, our computer-vision software analyses the images for distinctive landmarks in the environment, and uses these to restore balance.

All the image processing and control runs on a smartphone processor on board the drone. The onboard sensing and computation renders the drone safe and able to fly unaided. This allows the drone to fulfil its mission without any communication or interaction with the operator.

The recovery procedure consists of multiple stages. First, the quadrotor stabilizes its attitude and altitude, and then it re-initializes its visual state-estimation pipeline before stabilizing fully autonomously. To experimentally demonstrate the performance of our system, in the video we aggressively throw the quadrotor in the air by hand and have it recover and stabilize all by itself. We chose this example as it simulates conditions similar to failure recovery during aggressive flight. Our system was able to recover successfully in several hundred throws in both indoor and outdoor environments.

More info: Robotics and Perception Group, University of Zurich.


References

M. Faessler, F. Fontana, C. Forster, D. Scaramuzza. Automatic Re-Initialization and Failure Recovery for Aggressive Flight with a Monocular Vision-Based Quadrotor. IEEE International Conference on Robotics and Automation (ICRA), Seattle, 2015.

M. Faessler, F. Fontana, C. Forster, E. Mueggler, M. Pizzoli, D. Scaramuzza. Autonomous, Vision-based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle. Journal of Field Robotics, 2015.



If you liked this article, you may also be interested in:

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

 



tags: , , , , , ,


Matthias Fässler is a PhD student at Prof. Scaramuzza's Robotics and Perception Group of the University of Zurich.
Matthias Fässler is a PhD student at Prof. Scaramuzza's Robotics and Perception Group of the University of Zurich.

Flavio Fontana is a PhD candidate at the Robotics and Perception Group.
Flavio Fontana is a PhD candidate at the Robotics and Perception Group.

Elias Müggler is a PhD student at Prof. Scaramuzza's Robotics and Perception Group of the University of Zurich.
Elias Müggler is a PhD student at Prof. Scaramuzza's Robotics and Perception Group of the University of Zurich.

Christian Forster is a PhD student at the Robotics and Perception Group.
Christian Forster is a PhD student at the Robotics and Perception Group.

Davide Scaramuzza is Assistant Professor of Robotics at the University of Zurich.
Davide Scaramuzza is Assistant Professor of Robotics at the University of Zurich.

            AUAI is supported by:



Subscribe to Robohub newsletter on substack



Related posts :

Humanoid robots can sprint and box – but the real test is doing the laundry

  12 Oct 2026
No robot system can yet reliably handle the whole laundry process. Even folding remains a challenge.

Robot Talk Episode 165 – Robots behind the research, with Kushant Patel

  09 Oct 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Kushant Patel from Brookhaven National Laboratory about robots that work in scientific facilities, such as particle accelerators.

What three days building a robot taught me

and   07 Oct 2026
The brief sounded simple: build a small autonomous robot, then make it survive increasingly difficult mazes. Simple... right? Until you actually have to build one.

Research could guide the future design of social robots

  05 Oct 2026
Giving robot companions animal-like or anime-style faces and voices could maximise their emotional appeal to users, according to new research which could help guide the growing field of social robotics.

Robot Talk Episode 164 – Accelerating robot learning, with Michelle Lu

  02 Oct 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Michelle Lu from Vsim Technology about using simulation and artificial intelligence to teach robots new skills.

Robotics and automation? Informal reflections on familiar terms

IEEE experts reflect on what is meant by the term "robotics and automation".

Small, medium or large, a robotic fish maintains its swimming ability

  28 Sep 2026
Researchers at EPFL and New York University have developed a robotic fish that can be made in various sizes for studying shallow creeks to open water.

Robot Talk Episode 163 – Robots helping people, with Aaron Edsinger

  25 Sep 2026
In the first episode of Robot Talk season 7, Claire chatted to Aaron Edsinger from Hello Robot about their Stretch robots that can help people with mobility issues live more independently.


↑


AUAI is supported by:







Subscribe to Robohub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence