We have developed a computationally efficient trajectory generator for six degrees-of-freedom multirotor vehicles, i.e. vehicles that can independently control their position and attitude. The trajectory generator is capable of generating approximately 500’000 trajectories per second that guide the multirotor vehicle from any initial state, i.e. position, velocity and attitude, to any desired final state in a given time. In this video, we show an example application that requires the evaluation of a large number of trajectories in real time.

Multirotor vehicle

The multirotor vehicle used in the demonstration is an omni-directional eight-rotor vehicle. Its unique actuator configuration gives it full force and torque authority in all three dimensions, allowing it to fly novel maneuvers. For more details, please refer to the Youtube video or the research paper: “Design, Modeling and Control of an Omni-Directional Aerial Vehicle”, IEEE International Conference on Robotics and Automation (ICRA), 2016.

Researchers

Dario Brescianini and Raffaello D’Andrea
Institute for Dynamic Systems and Control (IDSC), ETH Zurich, Switzerland – http://www.idsc.ethz.ch

Location

Flying Machine Arena, ETH Zurich, Switzerland.

Acknowledgements

This work is supported by and builds upon prior contributions by numerous collaborators in the Flying Machine Arena project. See the list here. This research was supported by the Swiss National Science Foundation (SNSF).

Tailsitter in the Flying Machine Arena. Source: Robin Ritz, Institute for Dynamic Systems and Control, ETH Zurich.

In August 2014 Google [x] announced that they had been secretly developing a drone delivery program to rival Amazon’s Prime Air. Called Project Wing, the system was based on a mechanically simple “tailsitter” design – a hybrid vehicle that takes off vertically like a multirotor but is also able to fly horizontally like a fixed-wing airplane. Following field tests, however, Google scrapped the design, saying that it was difficult to control and didn’t perform well in high winds. Since then, researchers at ETH Zurich have developed a new algorithm for robustly controlling a tailsitter flying machine in hover position. Using the algorithm, the tailsitter is able to recover from any orientation, including upside down. ETH has not yet demonstrated its algorithm in real-world conditions (e.g., high winds, GPS localization, different payload distributions). However, given the robustness of the solution shown in the video, perhaps Google should not dismiss the tailsitter design too quickly for its next design iteration.  

– Robohub Editors

A tailsitter provides hover capability by pointing the nose and thrust direction upwards. For fast forward flight, the vehicle tilts to a near-horizontal orientation, resulting in efficient lift production due to the conventional wing design. Compared to other fixed-wing, hover-capable aircraft such as tiltrotors and tiltwings, the major advantage of a tailsitter is its mechanical simplicity: no mechanism for changing the direction of the propulsion system has to be added, saving weight and increasing reliability.

The algorithm demonstrated in the video is based on Optimal Control. Optimal trajectories for a set of initial orientations are precomputed and the corresponding initial desired body rates are stored in a small lookup table (500 entries). An outer control loop running at 50 Hz reads the desired body rates for the current orientation from the lookup table, and feeds them to an inner control loop. The inner control loop runs at 1000 Hz and tracks the desired body rates with the two propellers and the two flaps using feedback from a rate gyroscope. The resulting real-time computational complexity is extremely low.

Tailsitter’s propellers and motors. Source: Robin Ritz, Institute for Dynamic Systems and Control, ETH Zurich.

The propellers are driven by brushless Hacker A05-13S motors that are attached to the wings by 3D-printed mountings. While the vehicle is hovering, it is crucial that a large part of the flaps are within the propeller-induced airstream, in order to keep the flaps effective and the system highly maneuverable. The motors are controlled by ZTW Spider 12A ESCs, which run custom SimonK firmware that enables a fast response to thrust set point changes. Since the custom SimonK firmware is tuned for the fastest possible response, the motor controllers get quite hot and are therefore placed in the propeller airstream for cooling.

Tailsitter’s underbelly. Source: Robin Ritz, Institute for Dynamic Systems and Control, ETH Zurich.

Two radios are attached on the bottom side of the vehicle; a WiFly module for high-bandwidth telemetry, and a low-latency, frequency-hopping 2.4 GHz radio link that receives the control commands from the ground station. The total system latency including the motion capture system and the radio link is around 35 milliseconds.

Tailsitter’s flaps. Source: Robin Ritz, Institute for Dynamic Systems and Control, ETH Zurich.

The flaps are actuated by MKS DS65K servos providing a fast response on flap angle changes. The flaps of the original frame are designed for fixed-wing forward flight and do not provide enough control authority for the hover flight regime. Therefore their area is enlarged by carbon fibre sheets. This increases the effectiveness of the flaps by a factor of four. The maximum flap deviation is 30 degrees.

Tailsitter’s flight management unit (FMU). Source: Robin Ritz, Institute for Dynamic Systems and Control, ETH Zurich.

A Pixhawk PX4 Flight Management Unit (FMU) runs the onboard control loop that tracks the desired body rates using a rate gyroscope which is also provided by the FMU. Safety routines that switch off the actuators if the vehicle does not receive control commands anymore or if the battery voltage is too low are also implemented on the FMU.

The algorithm is presented in research paper “A Global Strategy for Tailsitter Hover Control”, submitted to International Symposium on Robotics Research (ISRR), 2015.

Researchers
Robin Ritz and Raffaello D’Andrea
Institute for Dynamic Systems and Control, ETH Zurich
Flying Machine Arena

Technical details
Frame: Robbe Mini Wing RC styrofoam airframe
Electronics: Pixhawk PX4 Flight Management Unit
Battery: Thunder Power RC G6 Pro Lite 25C LiPo 350mAh 2S
Motors: Hacker A05-13S
Motor controllers: ZTW Spider 12A ESCs with SimonK firmware
Propellers: 5×3 GWS EP-5030
Flap servos: MKS DS65K
Infrastructure: Flying Machine Arena

• Multiple tethered quadrotors whip around a pole together at high speeds 
• Quadrocopter failsafe algorithm: Recovery after propeller loss
• Three new quadrotor videos demonstrate agile control and the power of machine learning
• Video: Quadrocopter learns from its mistakes, perfects slalom air racing

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

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.

UPDATE 04/03/2014:

In this video update, we show that a quadrocopter can be safely piloted by hand after a motor fails, without the aid of a motion capture system. This follows our previous video, where we demonstrated how a complete propeller failure can be automatically detected, and that a quadrocopter can still maintain stable flight despite the complete loss of a propeller. 

In the earlier video, we relied on an external motion capture system to measure the quadrocopter’s position and orientation.  By moving more of the algorithm onto the vehicle, the quadrocopter can now be piloted by hand after the failure. The algorithm is executed on the quadrocopter’s onboard micro-controller, and the only sensors required are the quadrocopter’s angular rate gyroscopes. We use blinking LEDs, mounted on the quadrocopter’s arms, to indicate a virtual yaw angle, so that the pilot can control the vehicle with the same remote control commands after the failure. As an alternative to the LED system, an onboard magnetometer could be used to track the vehicle’s yaw angle. Alternatively, by using more sophisticated algorithms, the system could be made to work using only the rate gyroscopes.

ORIGINAL STORY 02/12/2013

The video in this article shows an automatic failsafe algorithm that allows a quadrocopter to gracefully cope with the loss of a propeller. The propeller was mounted without a nut, and thus eventually vibrates itself loose. The failure is detected automatically by the system, after which the vehicle recovers and returns to its original position. The vehicle finally executes a controlled, soft landing, on a user’s command.

The failsafe controller uses only hardware that is readily available on a standard quadrocopter, and could thus be implemented as an algorithmic-only upgrade to existing systems. Until now, the only way a multicopter could survive the loss of a propeller (or motor), is by having redundancy (e.g. hexacopters, octocopters). However, this redundancy comes at the cost of additional structural weight, reducing the vehicle’s useful payload. Using this technology, (more efficient) quadrocopters can be used in safety critical applications, because they still have the ability to gracefully recover from a motor/propeller failure.

The key functionality of the failsafe controller is a novel algorithm that I developed as part of my doctoral research at the Institute for Dynamic Systems and Control at ETH Zurich. This new approach allows such a vehicle to remain in flight despite the loss of one, two, or even three propellers. Having lost one (or more) propellers, the vehicle enters a continuous rotation — we then control the direction of this axis of rotation, and the total thrust that the vehicle produces, allowing us to control the vehicle’s acceleration and thus position.

Even if the vehicle can no longer produce sufficient thrust to support its own weight, this technology would still be useful: one could, for example, try to minimize the multicopter’s velocity when it hits the ground, or steer the multicopter away from dangerous situations such as water, or people on the ground.

This control approach can also be applied to design novel flying vehicles — we will be releasing some related results soon.

This technology is patent pending.

For more information, have a look at the Flying Machine Arena website, the IDSC research page, or just post your question in the comments below.

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.

The team at the ETH Flying Machine Arena has released three new videos, demonstrating quadrotors building tensile structures, tossing a ball back and forth, and refining a figure-eight trajectory using iterative learning. Worth the watch!!

Building Tensile Structures with Flying Machines

Federico Augugliaro: “This video shows quadrocopters assembling prototypical tensile structures. Part of a body of research in aerial construction – a field that addresses the construction of structures with the aid of flying machines – the video demonstrates that flying machines are capable of autonomously spanning a rope between two support points. They can also create surface structures by using already placed ropes as new support points. Furthermore, multiple machines can work together to extend the type of structures that can be created by this means. The project is a collaboration between the Institute for Dynamic Systems and Control and the Professorship for Architecture and Digital Fabrication, both from ETH Zurich, Switzerland.”

See Federico’s research paper, a collaboration with Ammar Mirjan, presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems 2013.

Rapid Trajectory Generation for Quadrotors

Mark Mueller: “We have developed a method for rapidly generating and evaluating quadrocopter interception trajectories. Each trajectory goes from an arbitrary initial state (position, velocity and acceleration) to an arbitrary final state. The evaluation of the trajectory includes determining input feasibility, and state feasibility (e.g. that the position of the quadrocopter remains inside a box). Per trajectory, this requires less than two microseconds on a modern laptop computer.

The trajectory generator is used here to generate trajectories to hit a ball towards a target, and determines:

•  when to hit the ball
•  how high to return the ball
•  how much thrust to use at the end of the trajectory.

The trajectory generator is used in a receding horizon implementation, where the optimization is run in closed loop at 50Hz, and only the first part of the trajectory is used as inputs. This allows the system to cope with sensor noise, and deviations in the ball’s flight path.”

See Mark’s research paper, presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems 2013.

Iterative Learning for Periodic Quadrocopter Maneuvers

Markus Hehn: “This video demonstrates an iterative learning algorithm that allows accurate trajectory tracking for quadrocopters executing periodic maneuvers.

The algorithm uses measurements from past executions in order to find corrections that lead to better tracking performance. In order to do this, we measure the tracking error over two laps of the maneuver. The new correction is then computed and applied. After waiting for one lap, we begin measuring again and the next learning step follows. For particularly dynamic maneuvers, we begin the learning process at lower execution speeds. This allows us to initially improve performance under safer conditions, and the algorithm provides a means to then transfer the learned corrections from the lower execution speed to higher speeds. The experience gained at lower speeds thus helps us when flying at high speeds, similar to how people learn skills such as martial arts or playing the piano. The method is also applicable to more complex tasks, shown here by the example of the quadrocopter balancing a pole while following a trajectory.”

See Markus’ research paper, presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems 2013.

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.

A quadrocopter swoops through the air to serve a glass of water without spilling a drop. Another gets two of its propellers cut off, yet still easily flies across the arena. “It looks like magic!” says ETH Zurich‘s Raffaello D’Andrea, but it took a lot of research, hard work and planning to bring this ‘magic trick’ to TED Global.
This post is part of Robohub’s TED Global 2013 coverage.

D’Andrea’s talk is about “Machine Athleticism” — the ability of machines to perform dynamic feats that fully exploit their physical capabilities. Part  of a session called “Those Flying Things” (which also features the work of drone ecologist Lian Pin Koh, airborne logistics activist Andreas Raptopoulos and sci-fi author Daniel Suarez), D’Andrea delves into what it takes to make a machine athlete:

We use something broadly called Model Based Design: we first build physics-based models that roughly capture the behavior of the machine; these models are then leveraged by a branch of mathematics called Control Theory to synthesize algorithms for controlling them.

He goes on to show a series of pretty amazing quadrotor feats that would be impossible without sophisticated automatic feedback. TED just posted an update on their blog about the demo here, and we will link to the video of the talk as soon as it goes online.

Clearly a lot of work has gone on behind the scenes to get D’Andrea and his team to TED. It’s taken years of research and a whole crew of PhD and Master students to develop the infrastructure and algorithms that make this demonstration possible — and that was just to get to the point where they could do the demos in-house.

D’Andrea and his team usually research and demo their quadrotor tricks at the ETH Flying Machine Arena — a 10x10x10m airspace dedicated to the study of control algorithms, and featuring a high-precision motion capture system, a wireless communication network, and custom software for executing estimation and control. Though the FMA normally lives at its home base at the Swiss Federal Institute of Technology (ETH) in Zurich, it was designed from the outset to be portable, and TED Global is not the first time it has taken to the road:
In 2011 they set up at the FRAC Centre in Orléans, France to create a six metre-high tower – the first architectural installation built by flying machines (see video). Last year the team showed their work at the world’s largest industrial fair, Germany’s Hannover Messe and at Google’s annual flagship conference Google IO in San Francisco.

Still, it takes a lot of time and careful planning to recreate the set up at a new site, and on this occasion they were planning a video shoot as well as a live audience. Says Mark Mueller, one of the team members onsite at TED, “Since April, all of us here have been working full-time, 100% on TED,” referring to all the various administrative hurdles they had to cross, such as planning logistics and agreeing on the space, in addition to rehearsing the demo itself. And then there was the packing to do: “It boils down to fifteen cases, boxes and bags,” says Dario Brescianinini, another member of the crew.

D’Andrea and his team arrived in Edinburgh, Scotland (the host city of TED Global 2013) late Saturday afternoon ahead of most other TED speakers, and started setting up almost right away in a dedicated room where the talk was filmed and where all their demonstrations will take place throughout the week. According to Brescianini and Mueller, it took about three-and-a-half hours to get the first quadrotor hovering. On Sunday they rehearsed the talk — and especially the transitions between each demo — in preparation for the video shoot, which was done in advance of the live talk.

Despite a few hiccups (two quads were damaged during setup and some repairs had to be done on the fly), Monday’s shoot went pretty smoothly. “It took about two-and-a-half hours, but we did one talk without breaks and had most of the things filmed after twenty-five minutes,” says Brescianini. It was a busy set, with fifteen TED crew members, seven people from D’Andrea’s team and one photographer. All in all, it took two fixed cameras (4k HD), one Steadicam, and three GoPros (one in the corner on the floor, one on the ceiling facing downwards, and one flying GoPro attached to a quad) to document D’Andrea’s TED talk. An additional camera shot a “making of” video as well. “It’s pretty busy, and we’re all really relieved after the successful filming.”

Want to learn more?
Check out the pages of ETH Zurich’s Flying Machine Arena team and more Robohub articles on how quadrocopters learn acrobatic maneuvers, cooperate to throw and catch balls, perfect air racing, and throw and catch an inverted pendulum.

Full disclosure: Markus Waibel works with Raff D’Andrea and the Flying Machine Arena team at ETH Zurich’s Institute for Dynamic Systems and Control.

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

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
• Video: Throwing and catching an inverted pendulum – with quadrocopters
• Quadrocopters learn from prior experience to improve slalom flying

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

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: