Resource Links:

http://www.mbtmag.com/videos/2013/08/flying-robots-new-data
Video Link: http://www.youtube.com/watch?v=PkW5h6NJuyA#at=77

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Links
Created by the team at

 http://www.kzoinnovations.com

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“Swarming Micro Air Vehicle Network (SMAVNET) Project”
“An autopilot controls altitude, airspeed and turn rate and chooses the most economical flight strategy”
“Although the robots are autonomous, they can be monitored and controlled through a swarm-interface running on a single PC”

Swarms of flying robots might sound a bit ominous to those of us anxiously awaiting the inevitable robot uprising that will see humanity drop a notch on the scale of planetary dominance. But swarms of flying robots are just what a project at the Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland is working to create. However, instead of keeping an eye on prisoners in a robot-run internment camp, the Swarming Micro Air Vehicle Network (SMAVNET) Project aims to develop robot swarms that can be deployed in disaster areas to rapidly create communication networks for rescuers.

The individual micro air vehicles (MAVs) are built out of Expanded Polypropylene (EPP) resulting in a weight of just 420g (14.8 ounces). With a wingspan of 80cm (31.5-inches) the MAVs have an electric motor mounted at the back and two control surfaces serving as elevons (combined ailerons and elevator). The robots run on a lithium polymer (LiPo) battery that provides 30 minutes of flying time.

Components

An autopilot controls altitude, airspeed and turn rate, while a micro-controller embedded in the autopilot chooses the most economical flight strategy based on input from three sensors: a gyroscope and two pressure sensors. The autopilot runs on a Toradex Colibri PXA270 CPU board running Linux, which is also connected to an off-the-shelf USB Wi-Fi dongle. In order to log flight trajectories, the robot is also equipped with a u-blox LEA-5H GPS module and a ZigBee (XBee PRO) transmitter.

As is so often the case, the SMAVNET designers turned to nature for inspiration in creating the behavior of the swarm. For the deployment and maintenance of retraction of the swarm MAV network the team turned to the army ants, which are able to lay and maintain pheromone paths from the nest to food sources. This method allows the swarm to maintain communication pathways between a base node and rescuers in the environment.

The robots use wireless communication between the robots themselves as a sensor instead of other methods that depend on the environment (GPS, cameras) or more expensive and heavy methods (lasers, radars). As the move out from their base to the users individual robots will hold position and form a node as the remainder of the swarm continues on until the objective is reached and the network complete. Although the robots are autonomous, they can be monitored and controlled through a swarm-interface running on a single PC.

Although 30 minutes of flight time might be somewhat limiting in a real world disaster situation, the EPFL team has conducted tests of the SMAVNET system that demonstrate its feasibility and provide encouragement for the team to continue with their efforts to create a low-cost system that could be deployed quickly in disaster hit areas.

External Links

http://www.epfl.ch/

The post SMAVNET Robots Create Communications Networks for Disaster Relief 31017 appeared first on Robotpark ACADEMY.

“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.

“Flying robots self-assemble into midair swarm”
“Individual vehicles self-assemble, coordinate, and take flight”

The Distributed Flight Array is a Swiss-built group of single-propeller robots that can autonomously dock with each other and hover above the ground. Is it the precursor to a flying robot swarm?

Swiss researchers are developing a robotic platform consisting of multiple single-propeller machines that autonomously dock with each other and take flight. The Distributed Flight Array, under development at the Swiss Federal Institute of Technology’s Institute for Dynamic Systems and Control (IDSC), may look like a kid’s remote-controlled toy, but it’s a neat example of swarm robotics.

Each vehicle is simply designed, with wheels for ground motion, one propeller, a computer, and infrared sensors that measure the flight angle. They join at random through magnetic links and drive around together. When it’s time to take off, the modules hover for a bit and then fly to a predetermined altitude. They exchange information over a network, maintaining level flight for the whole platform by adjusting individual thrust. The researchers seem barely able to regain control of their creation once it takes flight.

The IDSC researchers have shown in simulated and experimental tests that the array can work with anywhere from 2 to 20 propeller vehicles. But they’ve only flown up to 4 joined together so far. When it’s time to return to the ground, the modules come apart. Their sturdy plastic construction can withstand the impact of a fall from more than 6 feet.

The IDSC group has been developing the array since 2008. Last month, their study was named one of the best conference paper finalists at the IEEE International Conference on Robotics and Automation in Anchorage, Alaska.The researchers don’t mention possible applications for the Distributed Flight Array, but a glance at other IDSC projects such as the autonomously balancing cube shows the institute is open-minded enough to pursue whimsical, artistic endeavors when it comes to robots. Building a swarm of intelligent hunter-killer flying bots must be the farthest thing from their minds.

External Links

http://www.idsc.ethz.ch/Research_DAndrea/DFA

The post MODULAR, FLYING ROBOTIC SWARM – 31006 appeared first on Robotpark ACADEMY.

“WILL GOVERNMENT DRONES SPY ON CITIZENS”
“Hobby Drones Could Be Made Illegal in Texas”

There’s growing privacy concern over flying robots , or “drones”. Organizations like the EFF and ACLU have been raising the alarm over increased government surveillance of US citizens. Legislators haven’t been quick to respond to concerns of government spying on citizens. But Texas legislators are apparently quite concerned that private citizens operating hobby drones might spot environmental violations by businesses .

You may recall the story from 2012 in which a hobbyist operating a small UAV over public land in Dallas(1) , TX accidentally photographed a Dallas meat-packing plant illegally dumping pig blood into the Trinity river, resulting in an EPA indictment. Representative Lance Gooden has introduced HB912 to solve this “problem”. But the badly worded bill could also outlaw most outdoor hobby and STEM robotics activities, stop university robotics research programs, endanger commercial robotics R&D, and end many common commercial uses of robots such as commercial aerial photography. What exactly does the bill outlaw?

“A person commits an offense if the person uses or authorizes the use of an unmanned vehicle or aircraft to capture an image without the express consent of the person who owns or lawfully occupies the real property captured in the image.” (“Image” is defined as including any type of recorded telemetry from sensors that measure “sound waves, thermal, infrared, ultraviolet, visible light, or other electromagnetic waves, odor, or other conditions”.)

So any robot in the air, underwater, on the ground, even if operating on public property, that inadvertently records any type of sensor data originating on private property, is deemed illegal. The bill ignores long-standing legal precedent establishing 1st amendment protections for photography of private property and individuals from public land .

Todd Humphreys of the UTA Radionavigation Lab has warned, “the legislation is overly broad. It doesn’t allow for a distinction between intentional peeping toms and inadvertent or unwitting surveillance”. Ben Gielow of AUVSI has pointed out several illogical aspects of the bill including its odd focus on whether the photographer is inside a vehicle. For example, a Google street view car could photograph your house because the driver is in the vehicle but Google could not use a ground or air robot to take the same image because the photographer would be outside the vehicle. While it’s possible Gooden is simply technically illiterate when it comes to robotics, the more cynical view seems to be that the wording is intentional. The bill is worded to sound as if it prevents government drones from spying on citizens but then exempts most federal, state, and even local police spying under various circumstances. The bill also says: “an image captured in violation … may not be used as evidence in any criminal … proceeding” — which would have handily protected the meat-packing plant from that meddling citizen and his robot.

For more, see the Popular Science article ” Even Hobby Drones Could Be Made Illegal in Texas “. If you’re in Texas and concerned about this bill, there’s an FPVLAb discussion thread about it with information on contacting your representatives.

EXTRA NEWS

1-Dallas Meat Packing Plant Investigated after Drone Images Reveal Pollution

A good news drone story for a change. Showing once again just how useful simple platforms can be for aquiring imagery. Every environmental department really ought to have one.A Dallas sUAS enthusiast testing his camera equiped drone noticed something awry with the images he had taken. Speaking to sUAS News he said.

I was looking at images after the flight that showed a blood red creek and was thinking, could this really be what I think it is? Can you really do that, surely not?

Whatever it is, it was flat out gross. Then comes the question of who do I report this to that can find out what it is and where it is coming from.

Search after search and even some phone calls and I am not finding anything on who to call until I find the Nation Response Center. With their website saying that they are “the sole national point of contact for reporting all oil, chemical, radiological, biological and etiological discharges into the environment, anywhere in the United States and its territories” this sure seems like the correct place to start.

I tried to use their web reporting pages to report this, but there were question being asked that I just did not know, so I gave up and picked up the phone. The Coast Guard staffed 800 number was answered immediately, and I explained to the officer what I had seen and how. I asked if I had called the correct place, and was assured that I had. The officer took my report and asked me quite a few questions. I then asked what was going to happen from here, and I was told that the appropriate authorities, including the TCEQ (Texas Commision on Environment Quality) would be notified. A local investigator was dispatched within 20 minutes and onsite within another 20.

Last Thursday the EPA, TCEQ, and Texas Parks and Wildlife executed a search warrant.

Texas Environmental Crimes Task Force has watched the plant for two months after they first received the information. Dallas County has also been working with federal and state investigators ever since the tip came in.

Health and Human Services chief Zach Thompson says that’s what has county, federal, and state investigators so concerned. “Any time there is some type of discharge into the Trinity River… especially from an environmental standpoint, this is a real concern.”

“I think they discovered a secondary pipe again is my understanding, so the question is who installed the pipe and why was it there.”

The task force is investigating if the pig blood came from a secondary pipe not connected with the waste water system.

The post PRIVACY CONCERN OVER FLYING ROBOTS 31004 appeared first on Robotpark ACADEMY.

“Festo’s Flying Robot – SmartBird”
“An Incredible Flying Robot, Resembles a Real Bird”

Festo has added to its robotic menagerie with the creation of a robotic seagull that weighs just 450 g (15.87 oz) and boasts a wingspan of 1.96 m (6.4 ft). Dubbed the SmartBird, the ultralight flying robot was inspired by the herring gull and can take off, fly and land autonomously, without the help of any additional drive systems.

In creating the SmartBird, Festo says it has succeeded in deciphering the flight of birds. The robot’s wings not only beat up and down, with a lever mechanism increasing the degree of deflection to increase from the torso to the wing tip, but also twist at specific angles along their length in the same way that a real bird’s do so that the leading edge is directed upwards during the upward stroke.

Directional control is achieved through the opposing movement of the robot’s head and torso sections, which is synchronized by means of two electric motors and cables. This enables it to bend aerodynamically, with simultaneous weight displacement, and is responsible for the SmartBird’s agility and maneuverability.

As with a real bird, the SmartBird’s tail isn’t just for show either. It produces lift and functions as both a pitch elevator and yaw rudder. In addition to stabilizing the robot in a similar way to an aircraft’s conventional vertical stabilizer, the tail also tilts to initiate left and right turns and rotates about the longitudinal axis to produce yaw.

Packed inside the SmartBird’s torso are the battery, engine and transmission, the crank transmission and control and regulation electronics. Wing position and torsion can be monitored via two-way ZigBee protocol radio communication and can be adjusted and optimized in real time during flight.

Festo says developing the SmartBird has provided insights that will help it in a variety of areas. The robot’s minimal use of materials and lightweight construction will help increase efficiencies in resource and energy consumption, while the functional integration of its coupled drive units have provided ideas the company says it can transfer to the development of hybrid drive technology. Additionally, analysis of its flow characteristics during development has provided insights into ways to optimize future designs. Another plus is that it won’t try and steal your chips at the beach.

Videos

The post Incredible FLYING ROBOT, Festo SMARTBIRD 31003 appeared first on Robotpark ACADEMY.

Download Smart Bird PDF 

SmartBird is an ultralight but powerful flight model with excellent aerodynamic qualities and extreme agility. With SmartBird, Festo has succeeded in deciphering the flight of birds – one of the oldest dreams of humankind.

This bionic technology-bearer, which is inspired by the herring gull, can start, fly and land autonomously – with no additional drive mechanism. Its wings not only beat up and down, but also twist at specific angles. This is made possible by an active articulated torsional drive unit, which in combination with a complex control system attains an unprecedented level of efficiency in flight operation. Festo has thus succeeded for the first time in creating an energy-efficient technical adaptation of this model from nature.

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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.