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 :

Simulated zebrafish and a vision-equipped robotic fish reveal how the body shapes brain circuits

Researchers look to the fish brain to create a robotic zebrafish that can autonomously swim upstream.

Researchers develop modular nanorobot

  31 Jul 2026
A team at the University of Basel has developed a versatile nanorobot with propulsion and payload modules.

Surviving the paper deluge: a one-year study in learning from demonstration

With the explosion of robotics research, staying current in fields like Learning from Demonstration is a monumental challenge.

Soft robotic heart offers new way to study disease and test life-saving devices

Researchers have developed a soft robotic model of the human heart that can mimic disease and provide a realistic environment for testing the next generation of cardiac devices.

A mini robot to simplify dental treatment

  24 Jul 2026
Researchers have developed a miniature dental robot that could one day automatically prepare teeth for crowns.

Pressure-free growing robots for soft medical robotics

A Q&A with the best paper award winner at RoboSoft.

Interactive world simulator for robot policy training and evaluation

  20 Jul 2026
Yixuan Wang discusses his faithful world simulator that allows robots to learn how to push, pick up, and grasp objects.

Undergrads’ weed-killing robot wins top prize

  17 Jul 2026
Their robot can travel through a vineyard or orchard without a human operator, zapping weeds with a small amount of electricity.



AUAI is supported by:







Subscribe to Robohub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence