AirJelly’s environment is the air. Unlike AquaJelly, the remote-controlled jellyfish AirJelly does not swim through water, but instead glides instead through a sea of air thanks to its central electric drive unit and an intelligent, adaptive mechanism. It is able to do so because it consists of a helium-filled ballonett.

AirJelly‘s sole source of power is two lithium-ion polymer batteries connected to the central electric drive unit. It transmits the force to a bevel gear and from there to a succession of eight spur gears, which move the eight tentacles of the jellyfish via cranks. Each tentacle is designed as a structure with Fin Ray Effect^® . Propulsion of a ballonett by means of peristaltic motion is hitherto unknown in the history of aviation. AirJelly is the first indoor flight object with peristaltic drive. This new drive concept, with propulsion based on the principle of recoil, moves the jellyfish gently through the air.

Air Jelly PDF

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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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Together, the innovators invented and built the volocopter in a process that took over 2 years.  e-volo is the winner of the 2012 Lindbergh Prize for Innovation

Erik Lindbergh, grandson of Charles and Anne Morrow Lindbergh, announced the 2012 winner of the Lindbergh Prize. The Lindbergh Foundation’s aviation prizes are designed to recognize and stimulate innovation, and promote meaningful advancements in green aviation.

“We believe that the development of the Volocopter holds significant promise to radically change short distance transportation,” said Erik Lindbergh. “It has a long development path ahead, but if this innovative design reaches the commercial market it will dramatically change the way we move about the planet.”

What is a Volocopter?

The Volocopter by e-volo is a completely novel, vertical take-off and landing (VTOL) manned aircraft, which cannot be classified in any known category. The fact that it was conceived of as a purely electrically powered aircraft sets it apart from conventional aircraft.

Through the use of its many propellers, the Volocopter can take off and land vertically like a helicopter. A considerable advantage, apart from the simple construction without complex mechanics, is the redundancy of drives. This enables the safe landing of the volocopter even if some drives fail.

How Does the Volocopter Work?

The controls work according to the fly-by-wire principle very easily by means of a joystick. As opposed to any other aircraft, the operation is child’s play. It takes off and lands vertically and the pilot pays little or no attention to the flight path angle, minimum speed, stall, mixture control, pitch adjustment and many other things which make conventional aviation so demanding.

The propellers generate the entire ascending force, and by means of a selective change in rotary speed they simultaneously take care of the steering. Furthermore, as opposed to helicopters, no mechanical pitch control of the propellers is necessary whatsoever.

The automatic position control and the directional control take place by means of several independent and mutually monitoring airborne computers which control the rotation speed of each drive separately.

An optional, additional pusher propeller enables an even faster flight.

How Does the Volocopter Work?

The controls work according to the fly-by-wire principle very easily by means of a joystick. As opposed to any other aircraft, the operation is child’s play. It takes off and lands vertically and the pilot pays little or no attention to the flight path angle, minimum speed, stall, mixture control, pitch adjustment and many other things which make conventional aviation so demanding.

The propellers generate the entire ascending force, and by means of a selective change in rotary speed they simultaneously take care of the steering. Furthermore, as opposed to helicopters, no mechanical pitch control of the propellers is necessary whatsoever.

The automatic position control and the directional control take place by means of several independent and mutually monitoring airborne computers which control the rotation speed of each drive separately.

How Long Can The Volocopter Fly?

Currently, the limiting factor is the energy capacity of available batteries. However, a considerable advancement in battery technology is conceivable during the next few years, so that a multiplication of the energy capacity will occur within a short period of time. At present a battery flight time of 20 minutes is possible, but in the near future this will be extended to one hour or more.

To enable a flight time of several hours right from the start, our two-seater Volocopter is being developed as a serial hybrid electrical aircraft with a range extender.

A range extender is an additional aggregate in an electrical vehicle which extends the range of the vehicle considerably. The most commonly used range extenders are combustion motors which power a generator that supplies the batteries and electrical engines with electricity. Range extenders run at a constant rotation speed with optimal efficiency.

Concept Video VC007 (E-Volo)

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Resources

http://homepage2.nifty.com/smark/Habataki.htm

This ornithopter is now flying well. Look next – http://www.youtube.com/watch?v=rJwIhnFxuWQ
Flaptter 19-2 Second test Flight – http://www.youtube.com/watch?v=kzlY8gRs520

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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/

Resource Links

http://www.flyingmachinearena.org

Youtube Video – http://youtu.be/pcgvWhu8Arc

http://www.FlyingMachineArena.org

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Dragonfly drone flutters about, blows minds !

With the BionicOpter, Festo has technically mastered the highly complex flight characteristics of the dragonfly. Just like its model in nature, this ultralight flying object can fly in all directions, hover in mid-air and glide without beating its wings.

Festo isn’t quite the household name that Boston Dynamics is. (And, really, we’re not entirely sure Big Dog is a regular topic of conversation at dinner tables yet.) But, it certainly deserves just as much attention for the work they’re doing with robotics. After crafting a machine last year that soared around like a herring gull, now the company has created BionicOpter. The 17.3-inch long dragonfly drone can flutter through the air in any direction, and even hover, just like its biological inspiration. Its four carbon fiber and foil wings beat up to 20 times per-second, propelling it through the air as if it were swimming rather than flying.

Actually piloting the robo-bug is achieved through a smartphone app, but an on-board ARM-based microcontroller makes small adjustments to ensure stability during flight. There are a few important pieces of information we don’t have just yet. For one, it’s not clear how long the two-cell lithium ion battery will last, and pricing or availability are missing from the brochure (at the source link). Chances are though, you’ll never be able to afford one any way. Thankfully you can at least see this marvel of engineering in action after the break.

A Natural model for Flight

With the BionicOpter, Festo has applied these highly complex characteristics to an ultra-lightweight flying object at a technical level. For the first time, there is a model that can master more flight conditions than a helicopter, plane and glider combined.

In addition to controlling the flapping frequency and the twisting of the individual wings, each of the four wings features an amplitude controller. This means that the direction of thrust and the intensity of thrust for all four wings can be adjusted individually, thus enabling the remote-controlled dragonfly to move in almost any orientation in space. The intelligent kinematics correct any vibrations during flight and ensure flight stability both indoors and outdoors.

Integration of Functions in the Smallest of Spaces

The unique flight behaviour is made possible by the lightweight design of the model dragonfly and the integration of its functions:
sensors, actuators and mechanical components as well as communication, open and closed-loop control systems are installed ina very small space and connected to one another.

Thirteen Degrees of Freedom for Unique Flight Manoeuvres

In addition to control of the shared flapping frequency and twisting of the individual wings, each of the four wings also features an amplitude controller. The tilt of the wings determines the direction of thrust. Amplitude control allows the intensity of the thrust to be regulated. When combined, the remote-controlled dragonfly can assume almost any position in space.

Highly Integrated lightweight Design

This unique way of flying is made possible by the lightweight construction and the integration of functions: components such as sensors, actuators and mechanical components as well as open- and closed-loop control systems are installed in a very tight space and adapted to one another.

With the remote-controlled dragonfly, Festo demonstrates wireless real-time communication, a continuous exchange of information, as well as the ability to combine different sensor evaluations and identify complex events and critical states.

Highly Complex System with Easy Operation
Despite its complexity, the highly integrated system can be operated easily and intuitively via a smartphone. The flapping frequency,amplitude and installation angle are controlled by software and electronics; the pilot just has to steer the dragonfly – there is no need to coordinate the complex motion sequences.

Videos

Links

Find out more …

http://www.festo.com/en/bionicopter

http://www.festo.com/cms/en_corp/13165.htm

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Links
Created by the team at

 http://www.kzoinnovations.com

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But MIT’s Robust Robotics Group — which fielded the team that won the last AUVSI contest — has set itself an even tougher challenge: developing autonomous-control algorithms for the indoor flight of GPS-denied airplanes. At the 2011 International Conference on Robotics and Automation (ICRA), a team of researchers from the group described an algorithm for calculating a plane’s trajectory; in 2012, at the same conference, they presented an algorithm for determining its “state” — its location, physical orientation, velocity and acceleration. Now, the MIT researchers have completed a series of flight tests in which an autonomous robotic plane running their state-estimation algorithm successfully threaded its way among pillars in the parking garage under MIT’s Stata Center.

“The reason that we switched from the helicopter to the fixed-wing vehicle is that the fixed-wing vehicle is a more complicated and interesting problem, but also that it has a much longer flight time,” says Nick Roy, an associate professor of aeronautics and astronautics and head of the Robust Robotics Group. “The helicopter is working very hard just to keep itself in the air, and we wanted to be able to fly longer distances for longer periods of time.”

With the plane, the problem is more complicated because “it’s going much faster, and it can’t do arbitrary motions,” Roy says. “They can’t go sideways, they can’t hover, they have a stall speed.”

Found in Translation

To buy a little extra time for their algorithms to execute, and to ensure maneuverability in close quarters, the MIT researchers built their own plane from scratch. Adam Bry, a graduate student in the Department of Aeronautics and Astronautics (AeroAstro) and lead author on both ICRA papers, consulted with AeroAstro professor Mark Drela about the plane’s design. “He’s a guy who can design you a complete airplane in 10 minutes,” Bry says. “He probably doesn’t remember that he did it.” The plane that resulted has unusually short and broad wings, which allow it to fly at relatively low speeds and make tight turns but still afford it the cargo capacity to carry the electronics that run the researchers’ algorithms.

Because the problem of autonomous plane navigation in confined spaces is so difficult, and because it’s such a new area of research, the MIT team is initially giving its plane a leg up by providing it with an accurate digital map of its environment. That’s something that the helicopters in the AUVSI challenges don’t have: They have to build a map as they go.

But the plane still has to determine where it is on the map in real time, using data from a laser rangefinder and inertial sensors — accelerometers and gyroscopes — that it carries on board. It also has to deduce its orientation — how much it’s tilted in any direction — its velocity, and its acceleration. Because many of those properties are multidimensional, to determine its state at any moment, the plane has to calculate 15 different values.

That’s a massive computational challenge, but Bry, Roy and Abraham Bachrach — a grad student in electrical engineering and computer science who’s also in Roy’s group — solved it by combining two different types of state-estimation algorithms. One, called a particle filter, is very accurate but time consuming; the other, called a Kalman filter, is accurate only under certain limiting assumptions, but it’s very efficient. Algorithmically, the trick was to use the particle filter for only those variables that required it and then translate the results back into the language of the Kalman filter.

Confronting Doubt

To plot the plane’s trajectory, Bry and Roy adapted extremely efficient motion-planning algorithms (http://web.mit.edu/newsoffice/2011/smarter-robot-arms-0921.html) developed by AeroAstro professor Emilio Frazzoli’s Aerospace Robotics and Embedded Systems (ARES) Laboratory. The ARES algorithms, however, are designed to work with more reliable state information than a plane in flight can provide, so Bry and Roy had to add an extra variable to describe the probability that a state estimation was reliable, which made the geometry of the problem more complicated.

Paul Newman, a professor of information engineering at the University of Oxford and leader of Oxford’s Mobile Robotics Group, says that because autonomous plane navigation in confined spaces is such a new research area, the MIT team’s work is as valuable for the questions it raises as the answers it provides. “Looking beyond the obvious excellence in systems,” Newman says, the work “raises interesting questions which cannot be easily bypassed.”

But the answers are interesting, too, Newman says. “Navigation of lightweight, dynamic vehicles against rough prior 3-D structural maps is hard, important, timely and, I believe, will find exploitation in many, many fields,” he says. “Not many groups can pull it all together on a single platform.”

The MIT researchers’ next step will be to develop algorithms that can build a map of the plane’s environment on the fly. Roy says that the addition of visual information to the rangefinder’s measurements and the inertial data could make the problem more tractable. “There are definitely significant challenges to be solved,” Bry says. “But I think that it’s certainly possible.”

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