The testbed allows the quadrocopter’s high speed flight behavior to be characterized in order to determine drag characteristics, propeller efficiency, and the physical limits of the machine. It is also being used to safely develop high-speed maneuvers such as emergency braking.

Note that it is possible to remove the central pole by balancing the forces acting on the strings; this could be then used in performance settings, possibly enhanced by light and sound effects.

This research was conducted at the Flying Machine Arena at ETH Zurich.

Reference:

Maximilian Schulz, Federico Augugliaro, Robin Ritz, Raffaello D’Andrea , “High-speed, Steady Flight with a Quadrocopter in a Confined Environment Using a Tether “, IROS 2015, submitted.

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

• Three new quadrotor videos demonstrate agile control and the power of machine learning
• Quadrocopters learn from prior experience to improve slalom flying
• Surrounded by his quadrocopter drones on stage, Raffaello D’Andrea explains feedback control and talks about the coming Machine Revolution
• Video: Watch flying robots cooperate, throw and catch balls
• Quadrocopter failsafe algorithm: Recovery after propeller loss

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

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.

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

• Quadrocopter failsafe algorithm: Recovery after propeller loss
• Video: Throwing and catching an inverted pendulum – with quadrocopters
• Developing trust in autonomous robots: Seminar with Michael Wagner
• From sideshow to main event, drones are making their mark in entertainment

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

Check out live tweets, amazing photos, TED blog posts and awesome video coverage of the session Those Flying Things at this year’s TED Global, featuring the work of automation and controls expert Raffaello D’Andrea, and drone ecologist Lian Pin Koh. We will be adding new material here as it becomes available, so check back soon. Photo credit: James Duncan Davidson.

This post is part of Robohub’s TED Global 2013 coverage.

Update: TED video | Raffaello D’Andrea: The astounding athletic power of quadcopters

Update: TED video | Timelapse: Building an arena for flying robots
See the full TED Blog post for more about how James Duncan Davidson made the video.

Tweets about “quadrotor OR quadrocopter OR drone OR quadcopter OR quadcoptor OR quadrocoptor OR D’Andrea OR Raffaello OR Lian OR Pin OR Koh OR robot OR Suarez #TEDGlobal”

In his TED Global talk titled “Machine Athleticism” Raffaello D’Andrea shows how he uses control algorithms to push quadrotors to perform at their physical limits. Instead of relying on slides, D’Andrea (who is well-known for his quadrotor demos) demonstrates his flying machines live as they toss a ball, balance a poll, serve a glass of water without spilling a drop, and even fly with their props cut off. In his own words, D’Andrea’s talk is about “the ability of machines to perform dynamic feats that fully exploit their physical capabilities.”

Check out official TED blogger Kate Torgovnick for her coverage of Raffaello D’Andrea’s talk.

See all the posts in Robohub’s TED Global 2013 coverage.

Two of the most challenging problems tackled with quadrocopters so far are balancing an inverted pendulum and juggling balls. My colleagues at ETH Zurich’s Flying Machine Arena have now combined the two.

As part of his Master thesis Dario Brescianini, student at ETH Zurich’s Institute for Dynamic Systems and Control, has developed algorithms that allow quadrocopters to juggle an inverted pendulum . If you are not sure what that means (or how that is even possible), have a look at his video “Quadrocopter Pole Acrobatics”:

(Don’t miss the shock absorber blowing up in smoke at 1:34!)

The Math

A quadrocopter with a plate for balancing the pole. The cross-shaped cut-outs are used for easy attachment to the vehicle and have no influence on the pendulum’s stability.

To achieve this feat, Dario and his supervisors Markus Hehn and Raffaello D’Andrea started with a 2D mathematical model. The goal of the model was to understand what motion a quadrocopter would need to perform to throw the pendulum. In other words, what is required for the pendulum to lift off from the quadrocopter and become airborne?

This first step allowed to determine (theoretical) feasibility. In addition, it showed the ideal trajectory in terms of positions, speeds, and angles the quadrocopter needed to follow to throw a pendulum. And it offered an insight into the throwing process, including identification of its key design parameters.

Reality Checks
The main goal of the next step was to determine how well the theoretic model described reality: How well does the thrown pendulum’s motion match the mathematical prediction? Does the pendulum really leave the quadrocopter at the pre-computed time? How does the pendulum behave while airborne? How well do assumptions for catching the pendulum (e.g., completely inelastic collisions, completely rigid pendulum, infinite friction between quadrocopter and pendulum when balancing) hold?

This second step involved multiple tests with the physical system, including throwing the pendulum by hand to study its aerodynamic properties and precisely timing the quadrocopters’ and pendulum’s motions during the maneuver.

Analyze, Experiment, Repeat

The shock absorber at the end of the pendulum is a balloon filled with flour and attached to a sliding metal cap with zip ties.

Armed with a good theoretical model and knowledge of its strengths and limitations, the researchers set out on a process of engineering the complete system of balancing, throwing, catching, and re-balancing the pendulum. This involved leveraging the theoretic insights on the problem’s key design parameters to adapt the physical system. For example, they equipped both quadrocopters with a 12cm plate that could hold the pendulum while balancing and developed shock absorbers to add at the pendulum’s tips.

This also involved bringing the insights gained from their initial and many subsequent experiments to bear on their overall system design. For example, a learning algorithm was added to account for model inaccuracies.

Dario writes:

This project was very interesting because it combined various areas of current research and many complex questions had to be answered: How can the pole be launched off the quadrocopter? Where should it be caught and – more importantly – when? What happens at impact?

The biggest challenge to get the system running was the catching part. We tried various catching maneuvers, but none of them worked until we introduced a learning algorithm, which adapts parameters of the catching trajectory to eliminate systematic errors.

The long and iterative process of this third step resulted in the final successful architecture to repeatedly throw and catch the pendulum on the real system, including three key components:

First, a state estimator was used to accurately predict the pendulum’s motion while in flight. Unlike the ball used in the group’s earlier demonstration of quadrocopter juggling, the pendulum’s drag properties depend on its orientation. This means, among other things, that a pendulum in free fall will move sideways if oriented at an angle. Since experiments showed that this effect was quite large for the pendulum used, an estimator including a drag model of the pendulum was developed.
This was important to accurately estimate the pendulum’s catching position.

Another task of the estimator was to determine when the pendulum was in free flight and when it was in contact with a quadrocopter. This was important to switch the quadrocopter’s behavior from hovering to balancing the pendulum.

Second, a fast trajectory generator was needed to quickly move the catching quadrocopter to the estimated catching position.

Third, a learning algorithm was implemented to correct for deviations from the theoretical models for two key events: A first correction term was learnt for the desired catching point of the pendulum. This allowed to capture systematic model errors of the throwing quadrocopter’s trajectory and the pendulum’s flight. A second correction term was learnt for the catching quadrocopter’s position. This allowed to capture systematic model errors of the catching quadrocopter’s rapid movement to the catching position.

The Result
As you can see in the video embedded above, at the end of Dario’s thesis two quadrocopters could successfully throw and catch a pendulum.

Many of the key challenge of this work were caused by the highly dynamic nature of the demonstration. For example, the total time between a throw and a catch is a mere 0.65 seconds, which is a very short time to move to, and come to full rest at, a catching position.

Another key challenge was the demonstration’s high cost of failure: a failed catch typically resulted in the pendulum hitting a rotor blade, with very little chance for the catching quadrocopter to recover. A crashed quadrocopter not only entailed repairs (e.g., changing a propeller), but also meant recalibration of the vehicle to re-determine its operating parameters (e.g., actual center of mass, actual thrust produced by propellors) and restarting the learning algorithms.

Says Markus Hehn:

This was a really fun project to work on. We started off with some back-of-the-envelope calculations, wondering whether it would even be physically possible to throw and catch a pendulum. This told us that achieving this maneuver would really push the dynamic capabilities of the system.

As it turned out, it is probably the most challenging task we’ve had our quadrocopters do. With significantly less than one second to measure the pendulum flight and get the catching vehicle in place, it’s the combination of mathematical models with real-time trajectory generation, optimal control, and learning from previous iterations that allowed us to implement this.

Note: The Flying Machine Arena is an experimental lab space equipped with a motion capture system.
Full disclosure: I work with Dario Brescianini, Markus Hehn, and Raffaello D’Andrea at ETH Zurich’s Institute for Dynamic Systems and Control.

More photos:

Credits: Carolina Flores, ETH Zurich 2012

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

• Three new quadrotor videos demonstrate agile control and the power of machine learning
• Live at TED Global 2013: Tweets, photos and video for quadrotor fans
• Behind the scenes at TED Global: Raffaello D’Andrea and team demo amazing quadrotor “athletes”
• Quadrocopters learn from prior experience to improve slalom flying

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

During the 20 minute presentation, Raffaello D’Andrea revealed some of the key concepts behind his group’s impressive demonstrations of quadrocopters juggling, throwing and catching balls, dancing, and building structures – and illustrated them with live examples with quadrocopters flying on stage.

To watch him hurtle quadrocopters towards his audience, see them juggle balls and balance poles, and to find out what happens when control fails, check out the video:

Other speakers at the Zurich.Minds event included:

• Gerhard Schröder in an interview with host and organizer Rolf Dobelli (in German)
• Tomáš Sedláček on The Economics of Good and Evil
• Kevin Heng on Atmospheric Research of Exoplanets
• Ashkan Nikeghbali on The Fascination of Mathematics
• Lorenz Meier on the Open Hardware Revolution
• Christina Warinner on Evolutionary Diet
• Robert Cialdini on Influence
• Urs Hölzle on Where the Internet Lives, The Hidden Life of Data Centers
• Simone Schürle on Nano Robots for Medicine
• Lars Kolind on Unboss! The new leadership
• John Gray on The Dangers of Faith in Progress, and
• Benjamin Beilmann playing Prokofjew: Sonata for violin solo in D major, Op. 115 on his violin

Full disclosure: I, my colleagues from the Flying Machine Arena, and Raffaello D’Andrea all work in his group at the Institute for Dynamic Systems and Control at ETH Zurich.

Here are some photos of the event:

Photos: Zurich.Minds 2012

Manual programming of robots only gets you so far. And, as you can see in the video, for quadrocopters that’s not very far at all (see the “Without Learning” part starting at 1:30):

On its first try to navigate the obstacle course, the flying robot attempts to navigate based on a pre-computed flight path. The path is derived using a basic mathematical (“first principles”) model. But quadrocopters have complex aerodynamics – the force produced by the propellers changes depending on the vehicle’s velocity and orientation, and thus the actual amount of force produced is quite different from what the simple math describes.

What’s worse, these flying vehicles use soft propellers for safety, which bend differently depending on how much thrust is applied and wear rapidly with use (and even more rapidly when crashing).

Even with continuous feedback on the robot’s position from the motion capture system, manually programming the robots with a control sequence that takes all these imperfections into account is impractical.

My colleague Angela Schoellig and the Flying Machine Arena team here at ETH Zurich have now developed and implemented algorithms that allow their flying robots to race through an obstacle parcours – and learn to improve their performance.

Here is how Angela described the process to me:

The learning algorithm is applied to a quadrocopter that is guided by an underlying trajectory-following controller. The result of the learning is an adapted input trajectory in terms of desired positions. The algorithm has been equipped with several unique features that are particularly important when pushing vehicles to the limits of their dynamic capabilities and when applying the learning algorithm to increasingly complex systems:

1. We designed an input update rule that explicitly takes actuation and sensor limits into account by solving a constrained convex problem.
2. We developed an identification routine that extracts the model data required by the learning algorithm from a numerical simulation of the vehicle dynamics. That is, the algorithm is applicable to systems for which an analytical model is difficult (or impossible) to derive.
3. We combined model data and experimental data, traditional filtering methods and state-of-the- art optimization techniques to achieve an effective and computationally efficient learning strategy that achieves convergence in less than ten iterations.

The result is a robot that learns and improves each time it tries to perform a task.

In this example the robot races through a pylon parcours, calling to mind air races, such as the Red Bull Air Racing Championships or the Reno Air Races – except that there are no human pilots that spent their life learning to fly – here it’s robots doing the learning. And they are efficient, taking less than 10 training sessions to find the optimal steering commands!

Moreover, the learning algorithms are not specific to slalom racing, they can be used to learn other tasks. As Angela points out:

Our goal is to enable autonomous systems – such as the quadrocopter in the video – to ‘learn’ the way humans do: through practice.

The videos below show how learning algorithms can be used for other robotic tasks:

Full disclosure: Angela and the Flying Machine Arena team work in the same lab as I. Also, I’m working on RoboEarth, trying to allow robot learning on a much larger scale.

 
My colleagues at the ETH Zurich’s Flying Machine Arena have just released a new video. Here is what Robin Ritz, Lead Researcher on the project, has to say about it:
 

This video shows three quadrocopters cooperatively tossing and catching a ball with the aid of an elastic net.
 
To toss the ball, the quadrocopters accelerate rapidly outward to stretch the net tight between them and launch the ball up. Notice in the video that the quadrocopters are then pulled forcefully inward by the tension in the elastic net, and must rapidly stabilize in order to avoid a collision. Once recovered, the quadrotors cooperatively position the net below the ball in order to catch it.
 
Because they are coupled to each other by the net, the quadrocopters experience complex forces that push the vehicles to the limits of their dynamic capabilities. To exploit the full potential of the vehicles under these circumstances requires several novel algorithms, including:
 

1. an optimality-based real-time trajectory generation algorithm for the catching maneuver;
 
2. a time-varying trajectory following control strategy to manage the forces on the individual vehicles that are induced by the net; and
 
3. learning algorithms that compensate for model inaccuracies when aiming the ball.

 
For more information on this and related work, have a look at the Flying Machine Arena’s website or tune in to Prof. Raffaello D’Andrea’s ROBOTS Podcast interview.
 
Some photos of the robots in action: