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Danish Technological Institute's Centre for Robot Technology, one of Europe's leading innovators of robotics, is expanding with larger robot laboratories built in the science park Forskerparken in Odense. The new building takes part in strengthening the Centre’s ties to University of Southern Denmark, which is close by together with other of the Centre’s partner companies.

Getting oil out of water isn’t that hard, on principle. What is hard is getting a huge amount of oil out of an even huger amount of water. If you think about it, this is really a perfect task for a swarm of robots, since it’s simple and repeatable and just needs to be done over and over (and over and over and over) again. With this in mind, MIT’s Senseable City Lab has created Seaswarm, a swarm of networked oil spill cleanup robots:

The science behind gecko toes holds the answer to a dry adhesive that provides an ideal grip for robot feet. Stanford mechanical engineer Mark Cutkosky is using the new material, based on the structure of a gecko foot, to keep his robots climbing.

Can a team of soccer-playing robots beat the human World Cup champions by 2050? That's the ultimate goal of RoboCup, an international tournament where teams of soccer robots compete in various categories, from small wheeled boxes to adult-size humanoids. IEEE Spectrum's Harry Goldstein traveled to Singapore to attend RoboCup 2010 -- and check out how the man vs. machine future of soccer is playing out.

The Robotics Developer Studio (RDS) Simulator is a key feature of the package that allows you to get started without buying expensive robots. It is a great tool for use in education. The add-ons outlined below help you to create your own simulation environments and get started on learning about robotics.

You can think of the Distributed Flight Array as a combination between vertical take-off and landing vehicles, and modular reconfigurable robots. It is a flying platform consisting of multiple, autonomous, single-propeller vehicles, and these single propeller vehicles - or modules - are able to generate enough thrust to lift themselves into the air, but are completely unstable in flight, kind of like a helicopter without a tail rotor.

Scientists from Bielefeld University have come up with a plastic-head robot called Flobi that can express a number of different emotions, and can have it’s appearance reassembled from male to female (or vice-versa) in a couple of minutes.

A research project will be conducted this week in Linz, Austria, to discover what the ideal interaction between people and humanoid robots ought to be in the future, Honda R&D and Ars Electronica Futurelab announced today. The research, the first of its kind in Europe, will involve members of the public directly interacting with ASIMO, Honda's humanoid robot.

In its latest episode, the Robots Podcast interviews the lead researcher of the Distributed Flight Array and one of my colleagues at the ETH Zurich's IDSC, Raymond Oung. The Distributed Flight Array (DFA) is an aerial modular robot. Each individual module has a single, large propellor and a set of omniwheels to move around. Since a single propellor does not allow stable flight, modules move around to connect to each other. As shown in this video of the DFA, the resulting random shape then takes flight. After a few minutes of hovering the structure breaks up and modules fall back to the ground, restarting the cycle. As most projects at the IDSC, the DFA is grounded in rigorous mathematics and design principles and combines multiple goals: It serves as a real-world testbed for research in distributed estimation and control, it abstracts many of the real-world issues of the next generation of distributed multi-agent systems, and it provides an illustration for otherwise abstract concepts like distributed sensing and control to a general public. For more information on current work, future plans and real-world applications, read on or tune in!

It should be able to hold an inert grenade with one hand, and pull the pin with the other hand without the need for human control.  The software system must enable the robot to perform the Challenge Tasks following a high-level script with no operator intervention. For example, the operator would issue a command such as “Throw Ball.” That command would in turn decompose into a sequence of lower-level tasks, such as “find ball,” “grasp ball,” “re-grasp ball, cock arm, and throw.”

Honda and the Ars Electronica Futurelab are collaborating on a human-robot interaction study this week in Linz, Austria (September 2nd ~ 8th).  Although they say their goal is to determine how robots ought to interact with people in the future, I think this may be just an excuse to let the public have some one-on-one fun with ASIMO.  In any case, these sorts of studies should help steer Honda’s engineers in the right direction when designing the next version of the world’s most famous humanoid robot.

Ranger, a four legged bi-pedal robot, set an unofficial record at Cornell last month for walking 23 kilometers (14.3 miles), untethered, in 10 hours and 40 minutes. Walking at an average pace of 2.1 km/h (1.3 miles per hour), Ranger circled the indoor track at Cornell’s Barton Hall 108.5 times, taking 65,185  steps before it had to stop and recharge. Ranger walks much like a human, using gravity and momentum to help swing its legs forward, though its looks like a boom box on stilts. Its swinging gait is like a human on crutches since the robot has no knees, and its two exterior legs are connected at the top and its two interior legs are connected at the bottom.

Carnegie Mellon has taught its robotic snake to climb trees, though one hopes it won’t start offering your spouse apples. “Uncle Sam” (presumably named for its red, white, and blue markings) is a snake robot built from modular pieces. The latest in a line of ‘modsnakes’ from Carnegie Mellon’s Biorobotics Lab, Uncle Sam can move in a variety of different ways including rolling, wiggling, and side-winding. It can also wrap itself around a pole and climb vertically, which comes in handy when scaling a tree. You have to watch this thing in action. There is something incredibly life-like, and eerie, about the way it scales the tree outdoors and then looks around with its camera ‘eye’. Projects like Uncle Sam show how life-mimicking machines could revolutionize robotics in the near future.

Scientists at the Johns Hopkins University Applied Physics Laboratory (APL) were awarded no less than $34.5 million by the Defense Advanced Research Projects Agency (DARPA) to continue their outstanding work in the field of prosthetic limb testing, which has seen them come up with the most advanced model yet. Their Modular Prosthetic Limb (MPL) system is just about ready to be tested on human subjects, as it has proved successful with monkeys. Basically, the prosthetic arm is controlled by the brain through micro-arrays that are implanted (gently) in the head. They record brain signals and send the commands to the computer software that controls the arm. To be honest, it will be interesting to see just how these hair-chips are attached to the brain, but the APL say clinical tests have shown the devices to be entirely harmless. The monkeys didn’t mind them too much, at least.

SLAM ( Simultaneous localization and mapping ),  PID ( Proportional integral derivative ) controller & ODOMETRY ( hodos, meaning “travel“, “journey” and metron, meaning “measure“)

When we first reported on the Willow Garage PR2 robot, it was just a prototype. Earlier this year, Willow Garage started their beta program, which gave eleven lucky organizations two year access to PR2 robots in exchange for furthering work on open source robotics software. Now we've received word from Willow Garage that the PR2 is officially for sale to anyone who wants it. This is not a toy or hobby robot, of course, so don't expect a small price tag. The full retail price is $400,000 per unit. However, if your organization can demonstrate a proven track record in developing open source software and making contributions to the robotics community, you can get a hefty $120,000 discount on your PR2. For more see our previous stories on Willow Garage and the PR2.

Japanese robotics company HiBot has unveiled a nimble snake bot capable of moving inside air ducts and other narrow places where people can't, or don't want to, go. The ACM-R4H robot, designed for remote inspection and surveillance in confined environments, uses small wheels to move but it can slither and undulate and even raise its head like a cobra. The new robot, which is half a meter long and weighs in at 4.5 kilograms, carries a camera and LEDs on its head for image acquisition and can be fitted with other end-effectors such as mechanical grippers or thermo/infrared vision systems. Despite its seemingly complex motion capabilities, "the control of the robot is quite simple and doesn't require too much training," says robotics engineer and HiBot cofounder Michele Guarnieri.

Robot motion planning has traditionally been used to avoid collisions when moving a robot arm. Avoiding collisions is important, but many other desirable criteria are often ignored. For example, motions that minimize energy will let the robot extend its battery life. Smoother trajectories may cause less wear on motors and can be more aesthetically appealing. There may be even more useful criteria, like keeping a glass of water upright when moving it around. This summer, Mrinal Kalakrishnan from the Computational Learning and Motor Control Lab at USC worked on a new motion planner called STOMP, which stands for "Stochastic Trajectory Optimization for Motion Planning". This planner can plan paths for high-dimensional robotic systems that are collision-free, smooth, and can simultaneously satisfy task constraints, minimize energy consumption, or optimize other arbitrary criteria. STOMP is derived from gradient-free optimization and path integral reinforcement learning techniques (Policy Improvement with Path Integrals, Theodorou et al, 2010).

Marius Muja from University of British Columbia returned to Willow Garage this summer to continue his work object recognition. In addition to working on an object detector that can scale to a large number of objects, he has also been designing a general object recognition infrastructure. One problem that many object detectors have is that they get slower as they learn new objects. Ideally we want a robot that goes into an environment and is capable of collecting data and learning new objects by itself. In doing this, however, we don't want the robot to get progressively slower as it learns new objects. Marius worked on an object detector called Binarized Gradient Grid Pyramid (BiGGPy), which uses the gradient information from an image to match it to a set of learned object templates. The templates are organized into a template pyramid. This tree structure has low resolution templates at the root and higher resolution templates at each lower level. During detection, only a fraction of this tree must be explored. This results in big speedups and allows the detector to scale to a large number of objects.

The main focus for Bastian's work was on the feature-extraction process for 3D data. One of his contributions was a novel interest keypoint extraction method that operates on range images generated from arbitrary 3D point clouds. This method explicitly considers the borders of the objects identified by transitions from foreground to background. Bastian also developed a new feature descriptor type, called NARF (Normal Aligned Radial Features), that takes the same information into account. Based on these feature matches, Bastian then worked on a process to create a set of potential object poses and added spatial verification steps to assure these observations fit the sensor data.