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Nano&Micro Scale – Pop-up Fabrication of the Harvard Monolithic Bee (Mobee) – 11022

Gokhan Isgor — Sat, 26 Jan 2013 20:31:20 +0000

The Harvard Monolithic Bee is a millimeter-scale flapping wing robotic insect produced using Printed Circuit MEMS (PC-MEMS) techniques. This video describes the manufacturing process, including pop-up book inspired assembly. This work was funded by the NSF, the Wyss Institute, and the ASEE.

http://youtu.be/VxSs1kGZQqc

https://micro.seas.harvard.edu/publications.html (Publications Harward)

Check the articke below for detailed information about the video.

https://micro.seas.harvard.edu/papers/JMM11_Sreetharan_cover.pdf

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RHex Rough-Terrain Robot (Boston Dynamics) – 11019

Gokhan Isgor — Sat, 26 Jan 2013 14:25:44 +0000

RHex is a six-legged robot with inherently high mobility. Powerful, independently controlled legs produce specialized gaits that devour rough terrain with minimal operator input. RHex climbs in rock fields, mud, sand, vegetation, railroad tracks, telephone poles and up slopes and stairways.

RHex has a sealed body, making it fully operational in wet weather, muddy and swampy conditions. RHex’s remarkable terrain capabilities have been validated in government-run independent testing. RHex is controlled remotely from an operator control unit at distances up to 700 meters. Visible/IR cameras and illuminators provide front and rear views from the robot.

Terrain Specifications

Traverses rock fields, mud, sand, snow, gravel, 60% inclines and other rough terrain
• Crosses railroad tracks, curbs, logs and pipes
• Culvert inspection
• Climbs stairs
• Rugged modular design for maximum reliability in harsh conditions
• Operator control unit (OCU) with live video feed for remote operation
• IP Radio with 400-700m range
• Modular payload bay for mission specific packages

Specifications

RHex is a power – and computation – autonomous hexapod robot with compliant legs and only one actuator per leg. It is the first documented autonomous legged machine to have exhibited general mobility (speeds at bodylengths per second) over general terrain (variations in level at bodyheight scale). RHex is presently capable of speeds exceeding five body lengths per second (2.7 m/s), negotiates a wide variety of rugged terrains over thousands of bodylengths (3700 m distance on one set of batteries), manages slopes exceeding 45 degrees, swims, and climbs stairs.

Size:
Weight: 27.5lbs without batteries
Dimensions: 22”L x 16”W x 5.2”H (legs not extended)
Power:
Battery: Two BB2590 Batteries
Endurance: 6 hours
Mobility:
Entrapment Risk: Extremely low
Speed: over 2mph on natural terrain
Slopes: Up to 60% slope walk, up to 84% climb mode
Vertical Step: 8.5”
Water Ford Depth: 6”+
Environmental:
Temperature: -15C to 45C ambient continuous operation
Water: IP67 sealed, water submersible
Exposure: Tolerant of humidity, salt, oil, sand extremes
Cameras:
Fore and aft cameras and illuminators
Driving Resolution: 320×240 pixels
Still Image Resolution: 1280×960 pixels
Illuminators: Adjustable 6W Visible, 6W Infrared

11019-RHex-BostonDynamics2

Capabilities of RHex

Throughout its development, RHex acquired a large number of capabilities in its behavioral repertoire. In fact, it is the only robot that is capable of performing such a wide variety of behaviors as a single, autonomous robot. This performance is due to the significant amount of inspiration from the study of biological systems, leading to a number of principles underlying RHex’s design.

The use of legs instead of wheels or tracks opens the way for a large number of behaviors
Passive compliance in the legs overcomes limitations of underactuation and helps simplify mechanical design, yielding robustness
Sprawled posture, inspired from insects, results in passive stabilization of lateral motion
Control is open-loop at the gait level, but closed loop at the task level. Stability comes as a result of passive mechanics, not high-bandwidth active control

At the end of the project’s five years, RHex was capable of performing the following, mostly open-loop behaviors
–Running on reasonably flat, natural terrain at speeds up to 6 body lengths per second (just over 2.7 m/s)
–Climbing a wide range of stairs
–Climbing slopes up to 45 degrees
–Traverse obstacles as high as 20 cm (about twice RHex’s leg clearance)
–Continuously run for 45 minutes, covering up to 3 miles with an efficient gait
–Successfully traverse badly broken terrain with large rocks and obstacles
–Walk and run upside down
–Flip itself over to recover nominal body orientation
–Leaping across ditches up to 30 cm wide
–Support remote control from up to 150m distance

in addition to a number of behaviors that increasingly relied on feedback from sensors such as the onboard gyro, camera and strain gauges on the legs.

–Perform autonomous stabilization of yaw heading while running using feedback from the gyro
–Autonomously follow a line on the ground without any operator control
–Perform simultaneous localization and mapping by using artificial landmarks scattered over natural terrain
–Locomote on only two legs using active pendulum stabilization
–Autonomously change the rest lengths of its leg springs
–Autonomously run systematic experiments to tune its running gaits
–Use inertial sensors in combination with leg strain gauges to accurately estimate its body pose

JUMPING RHEX Light Robot

Move aside, Sand Flea, you’re not the only jumping robot in town. The researchers over at the University of Pennsylvania have taught their little six-legged X-RHex Light to make leaps and bounds as well, making it one of a few bots to both run and jump effectively.

While it can’t spring as high as the Boston Dynamics critter, the X-RHex can cross gaps with not just a bound but a running gait, given enough room. It can also flip itself over, climb onto a ledge with a double hop and perform a leaping grab to something as high as 73 centimeters (28.74 inches).

The X-RHex itself isn’t new; the curved-legged contraption has been around for at least a couple years, and even sported a cat-like tail for balance at one point. Still, the fact that the hefty 6.7 kilogram (14.8 pound) machine can now somersault through the air is a quite a victory, and one that reminds us of the impending robocalypse.

Video Links
Youtube- http://youtu.be/ISznqY3kESI
Youtube- http://youtu.be/kV9J-oayCBU

Resource Links

http://www.bostondynamics.com

http://www.engadget.com/2013/05/10/x-rhex-light-jumping-robot/

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Biology and Robotics – Robot Controlled With Rat Brain Cells – 11009

Gokhan Isgor — Fri, 25 Jan 2013 15:59:54 +0000

AFTER buttoning up a lab coat, snapping on surgical gloves and spraying them with alcohol, I am deemed sanitary enough to view a robot’s control system up close. Without such precautions, any fungal spores on my skin could infect it. “We’ve had that happen. They just stop working and die off,” says Mark Hammond, the system’s creator.

This is no ordinary robot control system – a plain old microchip connected to a circuit board. Instead, the controller nestles inside a small pot containing a pink broth of nutrients and antibiotics. Inside that pot, some 300,000 rat neurons have made – and continue to make – connections with each other.

http://youtu.be/1-0eZytv6Qk

How Does it Work ?

http://youtu.be/wACltn9QpCc

Nextworld: Rat Brain Controlled Robots

Kevin Lounsberry:

I would give my every possession to become a cyborg (though it might cost a bit more than that) chances are I could get it all back no sweat if I get good improvements. Imagine having things like built in like night vision or zooming or ultraviolet. Imagine it not even being possible to flinch and make a mistake with your hands. imagine the possibility of extra storage space for your memory, or removable drives.

http://youtu.be/RcQ7ACgihAg

Walking Around on the Floor

This robot is controlled by the brain of a rat – making it the world’s first cyborg rodent.
http://youtu.be/1QPiF4-iu6g

Rat’s ‘brain’ used to power robot

A robot has been created which is powered by a rat’s “brain”.

By Kate Devlin – 13 Aug 2008

Electrical signals from rat cells have been harnessed to drive the robot, which is on wheels, around a laboratory. By stimulating certain responses within the cells scientists have even been able to make the robot, or “animat”, move. The “brain” is actually rat brain tissue which has been artificially grown in a lab.

The scientists at Reading University hope that they can use the machine to understand more about how our brains work, and even to develop treatments for diseases such as epilepsy, Parkinson’s and Alzheimer’s Disease.

To create the machine scientists first grew rat nerve cells in a laboratory. These cells connect with each other, sending signals within around 24 hours. After a week the scientists can detect activity similar to brain activity. Within two or three weeks the cells can be hooked up to the robot. The team uses bluetooth technology, which allows them to send communication without the use of wires. Scientists can also use sonar signals to cause the robot to swerve to avoid a wall, by triggering different signals in the “brain”, reports New Scientist magazine.

The robots currently turn eight out of 10 times, but Professor Kevin Warwick, head of cybernetics at Reading University, who led the study, said that figure could increase substantially. He said: “[The animat] is actively learning. “The signals and the pathways are strengthening as each action gets repeated.” Prof Warwick said he believed that eventually the robot would turn 100 per cent of the time. He also hopes to use the animat to try to understand more about how the brain works, for example how it remembers things, by capturing the signals.

However, these “brains” have a limited lifespan and currently live for only around three months, as long as they are regularly fed in temperature controlled incubators. Prof Steve Potter, from the Georgia Institute of Technology, who has been involved in similar technology involving animals and robots, said that it was clear that brain cells have “evolved to reconnect under almost any circumstance that doesn’t kill them.”

http://www.telegraph.co.uk/news/2552973/Rats-brain-used-to-power-robot.html

Robot powered by rat’s brain in bizarre British experiment

It sounds like something out of a science fiction film, but British scientists have created a biological robot controlled by a blob of rat brain. The wheeled machine is wirelessly linked to a bundle of neurons kept at body temperature in a sterile cabinet. Signals from the ‘brain‘ allow the robot to steer left or right to avoid objects in its path. Researchers at the University of Reading are now trying to ‘teach’ the robot to become familiar with its surroundings. They hope the experiment will show how memories manifest themselves in nerve connections as the robot revisits territory it has been to before.

Scientists in other parts of the world are also developing robots with living brains made from cultured cells. At the Georgia Institute of Technology in Atlanta, US researchers have built a similar mobile machine. New Scientist magazine reported that the US team was training their robot as if it was an animal learning tricks.

The British research is led by Professor Kevin Warwick, who has pioneered the merging of biology and robotics by conducting bizarre ‘cyborg‘ experiments on himself. One involved embedding a microchip into the nerves of his left arm that allowed him to control an electric wheelchair and artificial hand.

The Reading robot’s brain consists of a small pot containing some 300,000 rat neurons. After first being disconnected, the nerves were then encouraged to make new connections with each other in a continuing process. The complex way neurons connect and ‘talk’ to each other is fundamental to how an organic brain works. Electrodes attached to the neural network allow sensory and command signals in and out of the brain. The robot has just one means of sensing its surroundings, an ultrasound probe that bounces sound waves off objects. If the sensor detects a wall in its path, a signal is sent to the brain through a Bluetooth radio link. The brain then replies with another message telling the robot to steer away from the obstacle.

The team is now moving away from this simple system and getting the robot to learn how to navigate. Eventually the robot will be able to recognise familiar surroundings it has memorised. Another aspect of the research is achieving a better understanding of conditions that affect the brain such as Alzheimer’s and Parkinson’s disease, and strokes. Prof Warwick said: ‘This new research is tremendously exciting as firstly the biological brain controls its own moving robot body, and secondly it will enable us to investigate how the brain learns and memorises its experiences.

‘This research will move our understanding forward of how brains work, and could have a profound effect on many areas of science and medicine.’ Colleague Dr Ben Whalley, from the university’s School of Pharmacy, said: ‘One of the fundamental questions that scientists are facing today is how we link the activity of individual neurons with the complex behaviours that we see in whole organisms. ‘This project gives us a really unique opportunity to look at something which may exhibit complex behaviours, but still remain closely tied to the activity of individual neurons. ‘Hopefully we can use that to go some of the way to answer some of these very fundamental questions.’

http://www.dailymail.co.uk/sciencetech/article-1044909/Robot-powered-rats-brain-bizarre-British-experiment.html

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Humanoid – Most Human Like Design – Fembot Actroid – 11007

Gokhan Isgor — Fri, 25 Jan 2013 04:31:03 +0000

This humanoid is the most human like design I have ever seen. It doesn’t walk, but it is focused on face design. Also it can move it’s arm and body.

Visit to Intelligent Robotics Laboratory – Repliqee Q2

http://youtu.be/Gv-_Vai9DgI

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4 Wheeled – Incredible Jumping – Can Jump up to 30 feet – 11006

Gokhan Isgor — Fri, 25 Jan 2013 04:29:27 +0000

Take care there is a trick here ! In the video 0:17 seconds they cut the scene and then 0:18 it jumps I will examine it later.

Sand Flea is an 11-lb robot with one trick up its sleeve: Normally it drives like an RC car, but when it needs to it can jump 30 feet into the air. An onboard stabilization system keeps it oriented during flight to improve the view from the video uplink and to control landings. Current development of Sand Flea is funded by the The US Army’s Rapid Equipping Force. For more information visit www.BostonDynamics.com.

http://youtu.be/6b4ZZQkcNEo

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Humanoid Robot Walking Like A Real Human – 11004

Gokhan Isgor — Fri, 25 Jan 2013 04:23:18 +0000

AIST has succeeded in making HRP-4C Miim walk like a human being. Her knees are stretched by up/down motion of the waist, the single-toe supporting realizes longer strides, and she mimics the swing motion of human legs.

Technical detail is presented in “Human-Like Walking with Toe Supporting for Humanoids,” by Kanako Miura, Mitsuharu Morisawa, Fumio Kanehiro, Shuuji Kajita, Kenji Kaneko, and Kazuhito Yokoi, Proc. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Related Links
AIST official Web site
http://www.aist.go.jp/index_en.html

Intelligent Systems Research Institute
http://unit.aist.go.jp/is/cie/index_e.html

http://youtu.be/YvbAqw0sk6M

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Cooperative Quadrocopter Ball Throwing and Catching – IDSC – ETH Zurich – 11003

Gokhan Isgor — Fri, 25 Jan 2013 04:17:09 +0000

This video shows three quadrocopters cooperatively tossing and catching a ball with the aid of an elastic net.

To toss the ball, the quadrocopters accelerate rapidly outward to stretch the net tight between them and launch the ball up. Notice in the video that the quadrocopters are then pulled forcefully inward by the tension in the elastic net, and must rapidly stabilize in order to avoid a collision. Once recovered, the quadrotors cooperatively position the net below the ball in order to catch it.

Because they are coupled to each other by the net, the quadrocopters experience complex forces that push the vehicles to the limits of their dynamic capabilities. To exploit the full potential of the vehicles under these circumstances requires several novel algorithms, including:

1) an optimality-based real-time trajectory generation algorithm for the catching maneuver;
2) a time-varying trajectory following control strategy to manage the forces on the individual vehicles that are induced by the net; and
3) learning algorithms that compensate for model inaccuracies when aiming the ball.

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/

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