Keywords: Nano, Silver, Ink, Inkjet, Printer, Printed Electronics, 31041

Printed Electronics by Robotpark

Researchers at “Robotpark R&D Labs” has developed an easy-to-use “conductive printer ink” which can print electronic boards with an ordinary inkjet printer. With this ink, you only draw your curcuit board in any drawing software, send it to your desktop printer and voila. “It is like magic...” says Harry Eliot one of the test engineers of the project.

After adding this ink to your desktop printers cartridge everything is ready. This process may be a little bit frustrating, but after you have successfully installed your cartridge, the magic begins.

The Nano Technology Beneath

The evolution of producing printed electronics — printing semiconducting organic polymers or conductive ink on paper or plastic to create electronically functional devices is a new approach.  These new manufacturing methods will play an increasingly important role in a wide range of applications. Silver Nano Particles which are smaller then 20nm are added to a special solvent, this makes the “Conductive Ink” but it is not finished. You have to use a special A4 Paper to ensure pattern integrity

Available in three different substrates, the self-sintering special inkjet media utilizes an adhesion layer, a micro-porous, solvent-absorbing layer for flexibility, print quality and instant dry benefits. The proprietary chemical sintering agents produce instant electrical conductivity and the resultant patterns look like gold foil stamping.

The Products

Robotpark’s Smart Conductive Series makes time-consuming and expensive thermal curing and other additional processes that are generally used to produce conductors no longer required.

Creating functional electronic devices faster, more easily and more cost-effectively is vital to research institutions, development engineers, students and electronical device developers. Applications, once too complex are now easy with Robotpark’s SCS (Smart Conductive Series) Silver Nano Inkjet Tech.

Whether it be in research labs or in test areas, development engineers can print with Robotpark’s SCS (Smart Conductive Series) on a variety of specially treated media for conductivity in seconds without heat or flash exposure sintering.

Links

You can order test samples with the following link if you are interested in this technology.
http://www.robotpark.com/Silver-Nano-Particle-Printer-Ink-10ml

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“Electrically-Charged Hydrogel has applications for soft robotics and biomedical fields”

Article Summary - By Randall Marsh
Soft robotics is a quickly emerging field that takes a lot of inspiration from marine creatures like squids and starfish. A light-controlled hydrogel was recently developed that could be used for control of these new robotic devices, but now researchers at North Carolina State University are taking the development of soft robotic devices to a new level with electrically-charged hydrogels.

What is IONOPRINTING ?

Researchers at NC State have developed a method called ‘ionoprinting’ with the capability to pattern and actuate hydrated gels in two and three dimensions by locally patterning ions using electric fields. The ability to pattern, structure, re-shape and actuate hydrogels is important for biomimetics, soft robotics, cell scaffolding and biomaterials.

The ionic binding changes the local mechanical properties of the gel to induce relief patterns and in some cases evokes localized stresses large enough to cause rapid folding. These ionoprinted patterns are stable for months, yet the ionoprinting process is fully reversible by immersing the gel in a chelator. The mechanically patterned hydrogels exhibit programmable temporal and spatial shape transitions and serve as a basis of a new class of soft actuators able to gently manipulate objects both in air and in liquid.

The paper, “Reversible patterning and actuation of hydrogels by electrically assisted ionoprinting” is co-authored by Etienne Palleau, Daniel Morales, Michael Dickey and Orlin Velev and published in Nature Communications. The work was supported by the National Science Foundation Triangle MRSEC program and the French DGA.

Resource Links:

http://ionoprinting.com/

http://www.nature.com/ncomms/2013/130802/ncomms3257/fig_tab/ncomms3257_F1.html

http://www.gizmag.com/electrically-charged-hydrogel-soft-robotics/28576/

Video – http://youtu.be/9SXWJP1KK-8

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The nanorobots—a collection of DNA molecules, some attached to antibodies—were designed to seek a specific set of human blood cells and attach a fluorescent tag to the cell surfaces. Details of the system were published online in Nature Nanotechnology.

“This opens up the possibility of using such molecules to target, treat or kill specific cells without affecting similar healthy cells,”

said the study’s senior investigator, Milan Stojanovic, PhD, assoc. prof. of medicine and of biomedical engineering at Columbia Univ. Medical Center.

“In our experiment, we tagged the cells with a fluorescent marker; but we could replace that with a drug or with a toxin to kill the cell.”

Though other DNA nanorobots have been designed to deliver drugs to cells, the advantage of Stojanovic’s fleet is its ability to distinguish cell populations that do not share a single distinctive feature.

Cells, including cancer cells, rarely possess a single, exclusive feature that sets them apart from all other cells. This makes it difficult to design drugs without side effects. Drugs can be designed to target cancer cells with a specific receptor, but healthy cells with the same receptor will also be targeted.

The only way to target cells more precisely is to identify cells based on a collection of features. “If we look for the presence of five, six or more proteins on the cell surface, we can be more selective,” Stojanovic said. Large cell-sorting machines have the ability to identify cells based on multiple proteins, but until now, molecular therapeutics have not had that capability.

How it works

Instead of building a single complex molecule to identify multiple features of a cell surface, Stojanovic and his colleagues at Columbia used a different, and potentially easier, approach based on multiple simple molecules, which together form a robot (or automaton, as the authors prefer calling it).

To identify a cell possessing three specific surface proteins, Stojanovic first constructed three different components for molecular robots. Each component consisted of a piece of double-stranded DNA attached to an antibody specific to one of the surface proteins. When these components are added to a collection of cells, the antibody portions of the robot bind to their respective proteins (in the figure, CD45, CD3 and CD8) and work in concert.

On cells where all three components are attached, the robot is functional and a fourth component (labeled 0 below) initiates a chain reaction among the DNA strands. Each component swaps a strand of DNA with another, until the end of the swap, when the last antibody obtains a strand of DNA that is fluorescently labeled.

At the end of the chain reaction—which takes less than 15 min in a sample of human blood—only cells with the three surface proteins are labeled with the fluorescent marker.

“We have demonstrated our concept with blood cells because their surface proteins are well known, but in principle our molecules could be deployed anywhere in the body,”

Stojanovic said. In addition, the system can be expanded to identify four, five, or even more surface proteins. Now the researchers must show that their molecular robots work in a living animal; the next step will be experiments in mice.

Links

http://newsroom.cumc.columbia.edu/2013/08/07/dna-robots-tag-cells/

http://www.rdmag.com/news/2013/08/dna-robots-find-and-tag-blood-cells

The post DNA Robots Find and Tag Blood Cells 31036 appeared first on Robotpark ACADEMY.

Motivation: Interest in fabricating large numbers of small robots has grown recently due to applications ranging from mobile sensor networks to search and rescue. However, realizing these applications is difficult due to the extended fabrication time, cost, and fragility of current robot manufacture and design. Several mobile robots have been demonstrated at the centimeter-scale, but they generally lack robustness and cannot be easily manufactured in large numbers. These robots also require high one-time equipment costs and can take a day or more to assemble.

Objectives: The process outlined in this work uses inexpensive, compliant photo-patternable materials with the ability to embed components to combine the benefits of small-scale robots with the robustness and compliance improvements in larger-scale robots. Compliant mechanisms will improve the mobility and robustness of robots on the centimeter and millimeter-scales and can also be used to add mechanical energy storage for improved efficiency. Finally, the use of these polymers will allow many milli-robots to be fabricated in less than an hour on a benchtop instead of several weeks in a clean room or after many hours of assembly. While this process is currently limited to planar structures, separately constructed components can be stacked and folded to create more complex robots.

The objectives of this project are:
1. Development of a fabrication process that incorporates multiple UV-curable polymers with different material properties to create complex robot mechanisms.
2. Reduce feature sizes below 100 microns.
3. Robust integration of efficient actuators with robot mechanisms.
4. Test new locomotion methods to better study and understand efficient and effective locomotion at the sub-cm scale.

Overview of Approach: The milli robot prototyping process is described in the figure and video below. In addition, several mobile robots have already been fabricated and tested in this process.

Contact : 

Dr. Sarah Bergbreiter

Department of Mechanical Engineering and Institute for Systems Research
2170 Martin Hall, University of Maryland, College Park, MD-20742
Phone: 301-405-6506,  Email: sarahb@umd.edu

Links: 

http://robotics.umd.edu/research/projects/Bergbreiter_milli-robot.php

The post Multi Material POLYMER PROTOTYPING for MICRO Robots 11103 appeared first on Robotpark ACADEMY.

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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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.”

The post Autonomous Robotic Plane Flies Indoors at MIT 31025 appeared first on Robotpark ACADEMY.

Links

http://youtu.be/0KUdyBm6bcY

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Having already broken new ground in robotics with the development, last year, of a class of “soft”, silicone-based robots based on creatures like squid and octopi, Harvard scientists are now working to create systems that would allow the robots to camouflage themselves, or stand out in their environment.

As described in a paper published August 16 in Science, a team of researchers led by George M. Whitesides, the Woodford L. and Ann A. Flowers University Professor, has developed a “dynamic coloration” system for soft robots that might one day have applications ranging from helping doctors plan complex surgeries to acting as a visual marker to help search crews following a disaster.

In this video, Stephen Morin, a Post-Doctoral Fellow in Chemistry and Chemical Biology and first author of the paper, discusses the research and demonstrates how the system works.

Links

http://www.youtube.com/watch?v=bC1WFU4G-WU

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“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.

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