“Quadrocopters throw, catch, and balance an inverted pendulum”
 “The incredible precision flying achieved by Quadrocopters”
“Quadrocopter Pole Acrobatics”

Apparently, balancing a pole on top of a flying quadrocopter robot wasn’t challenging enough for the researchers at ETH Zurich’s Institute for Dynamic Systems and Control. Their latest project has two quadrocopters playing catch with a precariously balanced pole – the first robot launches the pole into the air, while the second robot deftly moves into position in less than a second to catch it as it falls. The incredible precision flying achieved by the team can be seen in a video after the break.

The work, appropriately titled “Quadrocopter Pole Acrobatics,” was done by Dario Brescianini as part of his master thesis under the supervision of Markus Hehn and Raffaello D’Andrea at ETH Zurich’s Flying Machine Arena – a special lab designed specifically for testing advanced flying maneuvers with quadrocopters. We’ve covered some of the lab’s work before, including one example where three quadrocopters attached to a net used it to launch and catch a ball, which we thought was pretty impressive … until we saw this.

They began with a 2D mathematical model that described how a quadrocopter would need to fly (including its speed and trajectory) in order to launch a pole it was balancing into the air. They then tested the model’s accuracy on the physical robot, including how the airborne pendulum actually moves. They found that the pole’s drag properties changed depending on its orientation, and so developed a state estimator to account for it.

The project’s caveats include 12-cm (4.7-inch) discs attached to each robot (that serve as the balancing platforms) and the addition of balloons filled with flour on either end of the pendulum to serve as simple shock absorbers (you can see one explode at 94 seconds in the video below). These minor modifications make the job a tad easier, but don’t diminish the demonstration’s wow factor.

“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?” Brescianini told RoboHub. “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.”

To successfully position the catching robot, the team developed a fast trajectory generator that could estimate the precise catching position in less than 0.65 seconds – the short time it takes complete the entire move. Early tests were hampered by mid-air collisions between the pole and the quadrocopter’s delicate propellers, which resulted in time-consuming repairs and recalibration between experiments.

“As it turned out, it is probably the most challenging task we’ve had our quadrocopters do,” added Hehn. “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.”

It may not be the most practical application for flying robots, but we won’t know what these types of systems can do unless we put them to the test.

The post Quadrocopters Balance Show Throw and Catch 31009 appeared first on Robotpark ACADEMY.

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.

By Robin Ritz, Mark W. Müller, Markus Hehn, and Raffaello D’Andrea.
IDSC, ETH Zürich, Switzerland
http://www.flyingmachinearena.org

This work is supported by and builds upon prior contributions by past and present FMA collaborators.
http://www.idsc.ethz.ch/Research_DAndrea/FMA/participants

Designers of this Quadrocopters  – Flying Machine Arena

ABOUT - Flying Machine Arena

The Flying Machine Arena (FMA) is a portable space devoted to autonomous flight. Measuring up to 10 x 10 x 10 meters, it consists of a high-precision motion capture system, a wireless communication network, and custom software executing sophisticated algorithms for estimation and control.

The motion capture system can locate multiple objects in the space at rates exceeding 200 frames per second. While this may seem extremely fast, the objects in the space can move at speeds in excess of 10 m/s, resulting in displacements of over 5 cm between successive snapshots. This information is fused with other data and models of the system dynamics to predict the state of the objects into the future.

The system uses this knowledge to determine what commands the vehicles should execute next to achieve their desired behavior, such as performing high-speed flips, balancing objects, building structures, or engaging in a game of paddle-ball. Then, via wireless links, the system sends the commands to the vehicles, which execute them with the aid of on-board computers and sensors such as rate gyros and accelerometers.

Although various objects can fly in the FMA, the machine of choice is the quadrocopter due to its agility, its mechanical simplicity and robustness, and its ability to hover. Furthermore, the quadrocopter is a great platform for research in adaptation and learning: it has well understood, low order first-principle models near hover, but is difficult to characterize when performing high-speed maneuvers due to complex aerodynamic effects. We cope with the difficult to model effects with algorithms that use first-principle models to roughly determine what a vehicle should do to perform a given task, and then learn and adapt based on flight data.

HISTORY - Flying Machine Arena 

The genesis of the Flying Machine Arena (FMA) can be traced to various research projects that date back to the 1990s. The system architecture for the FMA, for example, is the same architecture that was used for Cornell University’s Robot Soccer Team in 1998. Founded by Raffaello D’Andrea, the Cornell team featured vehicles with rudimentary local intelligence, an overhead vision system (which acted as a surrogate for GPS), a high-performance workstation for implementing computationally intensive tasks such as path planning, and a wireless link for sending commands to the vehicles.

After Cornell won the 1999 RoboCup competition in Stockholm, D’Andrea and his research team began to explore the possibility of extending the system beyond the soccer pitch and into the third dimension. Despite lacking essential technology for conducting this kind of research, they built a series of high-performance aerial vehicles, developed systems to track and control them, and made plans to construct a test-bed in which to house it all.

In 2000, they built a quadrocopter prototype (pictured below), mounted LEDs on it, and used three cameras to determine the vehicle position and attitude. Engineering student Andy Eichelberger developed the first version of the system as part of his Master of Engineering degree, which was then refined and used by Matt Earl as part of his PhD thesis.

In 2002, Master of Science students Eryk Nice and Sean Breheny began to build a high performance quadrocopter (pictured below), which was then used by Oliver Purwin for his PhD research. With propellers that were each 45cm in diameter, this vehicle was much larger than the first one, and could consume over 4000 watts of power at peak thrust. The vehicle’s high performance inertial measurement unit (the gold box in the middle of the quadrocopter) weighed more than 1kg, and was responsible for driving the vehicle’s size requirements.

In 2003, D’Andrea’s research team at Cornell received approval to convert the university’s High Voltage Laboratory – an empty 15,000 square foot building with 50-foot ceilings – into the Cornell Laboratory for Intelligent Vehicles. The goal was to transform the space into a test-bed for high performance air and ground vehicle control. At the same time, however, D’Andrea began a sabbatical to co-found Kiva Systems with partners Mick Mountz and Peter Wurman, and as a result the plans were abandoned. It has since become a large space for student projects.

Five years later, at the end of 2007, Kiva Systems was well on its way to becoming a successful robotics and logistics company, and D’Andrea decided to rejoin the academic world at ETH Zurich. The conditions for his appointment were predicated on the construction of a large, indoor space for flying vehicles: the Flying Machine Arena.

D’Andrea considers the five-year delay to be a blessing: in the interim, high-performance motion capture systems for implementing indoor GPS functionality had come into the marketplace; accurate solid-state accelerometers and rate gyros had become widely available (replacing large and expensive units with similar functionality); powerful rare earth magnet motors also became popular in this time period, resulting in high thrust-to-weight ratios for the power stages; and finally, wireless communication had become more reliable and easier to integrate into a multi-vehicle system. Says D’Andrea, “The time for the FMA had finally arrived.”

Contact Information

http://www.flyingmachinearena.org/contact/

The post Cooperative Quadrocopter Ball Throwing and Catching – IDSC – ETH Zurich – 11003 appeared first on Robotpark ACADEMY.

In the first part of the video, the destination points are selected ahead of time and collision-free trajectories are pre-computed. All the trajectories are stored before execution. In the second part of the video, however, the next set of destination points is picked at random while the vehicles are still en-route, demonstrating that the algorithm is fast enough to be used in real-time.

* Project by
Federico Augugliaro, Angela Schoellig and Raffaello D’Andrea
Institute for Dynamic Systems and Control, ETH Zurich, Switzerland

* Based on the work by
Yang Wang, Ekine Akuiyibo, Stephen Boyd
Information Systems Laboratory, Stanford University, USA

* Filmed at ETH Flying Machine Arena
http://www.FlyingMachineArena.org

Designers of this Quadrocopters  – Flying Machine Arena

ABOUT - Flying Machine Arena

The Flying Machine Arena (FMA) is a portable space devoted to autonomous flight. Measuring up to 10 x 10 x 10 meters, it consists of a high-precision motion capture system, a wireless communication network, and custom software executing sophisticated algorithms for estimation and control.

The motion capture system can locate multiple objects in the space at rates exceeding 200 frames per second. While this may seem extremely fast, the objects in the space can move at speeds in excess of 10 m/s, resulting in displacements of over 5 cm between successive snapshots. This information is fused with other data and models of the system dynamics to predict the state of the objects into the future.

The system uses this knowledge to determine what commands the vehicles should execute next to achieve their desired behavior, such as performing high-speed flips, balancing objects, building structures, or engaging in a game of paddle-ball. Then, via wireless links, the system sends the commands to the vehicles, which execute them with the aid of on-board computers and sensors such as rate gyros and accelerometers.

Although various objects can fly in the FMA, the machine of choice is the quadrocopter due to its agility, its mechanical simplicity and robustness, and its ability to hover. Furthermore, the quadrocopter is a great platform for research in adaptation and learning: it has well understood, low order first-principle models near hover, but is difficult to characterize when performing high-speed maneuvers due to complex aerodynamic effects. We cope with the difficult to model effects with algorithms that use first-principle models to roughly determine what a vehicle should do to perform a given task, and then learn and adapt based on flight data.

HISTORY - Flying Machine Arena 

The genesis of the Flying Machine Arena (FMA) can be traced to various research projects that date back to the 1990s. The system architecture for the FMA, for example, is the same architecture that was used for Cornell University’s Robot Soccer Team in 1998. Founded by Raffaello D’Andrea, the Cornell team featured vehicles with rudimentary local intelligence, an overhead vision system (which acted as a surrogate for GPS), a high-performance workstation for implementing computationally intensive tasks such as path planning, and a wireless link for sending commands to the vehicles.

After Cornell won the 1999 RoboCup competition in Stockholm, D’Andrea and his research team began to explore the possibility of extending the system beyond the soccer pitch and into the third dimension. Despite lacking essential technology for conducting this kind of research, they built a series of high-performance aerial vehicles, developed systems to track and control them, and made plans to construct a test-bed in which to house it all.

In 2000, they built a quadrocopter prototype (pictured below), mounted LEDs on it, and used three cameras to determine the vehicle position and attitude. Engineering student Andy Eichelberger developed the first version of the system as part of his Master of Engineering degree, which was then refined and used by Matt Earl as part of his PhD thesis.

In 2002, Master of Science students Eryk Nice and Sean Breheny began to build a high performance quadrocopter (pictured below), which was then used by Oliver Purwin for his PhD research. With propellers that were each 45cm in diameter, this vehicle was much larger than the first one, and could consume over 4000 watts of power at peak thrust. The vehicle’s high performance inertial measurement unit (the gold box in the middle of the quadrocopter) weighed more than 1kg, and was responsible for driving the vehicle’s size requirements.

In 2003, D’Andrea’s research team at Cornell received approval to convert the university’s High Voltage Laboratory – an empty 15,000 square foot building with 50-foot ceilings – into the Cornell Laboratory for Intelligent Vehicles. The goal was to transform the space into a test-bed for high performance air and ground vehicle control. At the same time, however, D’Andrea began a sabbatical to co-found Kiva Systems with partners Mick Mountz and Peter Wurman, and as a result the plans were abandoned. It has since become a large space for student projects.

Five years later, at the end of 2007, Kiva Systems was well on its way to becoming a successful robotics and logistics company, and D’Andrea decided to rejoin the academic world at ETH Zurich. The conditions for his appointment were predicated on the construction of a large, indoor space for flying vehicles: the Flying Machine Arena.

D’Andrea considers the five-year delay to be a blessing: in the interim, high-performance motion capture systems for implementing indoor GPS functionality had come into the marketplace; accurate solid-state accelerometers and rate gyros had become widely available (replacing large and expensive units with similar functionality); powerful rare earth magnet motors also became popular in this time period, resulting in high thrust-to-weight ratios for the power stages; and finally, wireless communication had become more reliable and easier to integrate into a multi-vehicle system. Says D’Andrea, “The time for the FMA had finally arrived.”

Contact Information

http://www.flyingmachinearena.org/contact/

The post Quadrocopters- Fast Transitions of a Quadrocopter Fleet Using Convex Optimization – 11002 appeared first on Robotpark ACADEMY.

Video Links
Watch On Youtube

Resource Links
http://www.flyingmachinearena.org

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