The war in Iraq may be over (to a large degree at least), but US troops are still stationed in combative places around the world, such as Afghanistan. One problem when it comes to supporting the troops in such distant places with hard to reach terrains is providing them with the supplies they need, an operation that is usually carried out by a helicopter, putting a pilot‘s life in danger. That is the why the US Army staged a competition for developers to come up with a functional unmanned helicopter that can deliver heavy cargo on its own.

The winner was Lockheed Martin’s submission, which took an interesting approach. Instead of building an entire new robotic helicopter, they took an existing craft and gave it important modifications. Other submissions for example also presented unnamed aircrafts, but those eliminated even the possibility of using a real human pilot. Lockheed Martin’s Kanman K-Max cargo helicopter, on the other hand, allows for that choice to be made based on each specific mission, so it is much more adaptable. It is capable of lifting 6000 pounds, flying over 200km and delivering its cargo within a 10 meter drop zone. The aircraft itself weighs 6k pounds, so it can lift its own weight. It is controlled from the ground, and in the video you can see a team with a playstation 2-like controller moving it in different directions.

Besides the K-Max, Lockhead Martin also came up with the JATAS prototype system, which warns pilots of attacks in hostile environments. It uses laser sensors to detect missiles and hostile fire, alarming the helicopters in (hopefully) ample time to prevent the hit.

The video focuses on the K-max and shows the unmanned helicopter during the tests, where it drops cargo and flies away in a swift and timely manner.

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Mobile robots have been used for years by the military and law enforcement, but with falling costs, the next frontiers are the office, the hospital and the home. SACRAMENTO — Dr. Alan Shatzel’s pager beeped at 9 on a Saturday morning. Dr. Shatzel, a neurologist, hustled not to the emergency room where the patient lay — 260 miles away, in Bakersfield — but to a darkened room at a hospital here. He guided the roughly five-foot-tall machine, which has a large monitor as its “head,” into the patient’s room in Bakersfield. “Computers are beginning to grow wheels and roll around in the environment,” said Jeanne Dietsch, a veteran roboticist and co-founder of MobileRobots Inc ., a robot maker in Amherst, N.H., and a division of Adept Technologies.

For the past three weeks I have been baby-sitting a robot. It looks like an extra-large lollipop on wheels and using it is a little like owning a puppy. For the past three weeks I have been baby-sitting a robot. It is not your traditional looking robot — if there is such a thing yet — but looks like an extra-large lollipop on wheels. People ask, “Can it do any neat tricks?” The robot I adopted, the Texai, is made by Willow Garage, a technology company based in California, and is designed for telepresence. I wheeled past, said hello over the robot’s speakers and watched through the camera as her head moved slowly with my passing, the spoon still suspended in midair.

Kernel mapping is one of the most used approaches to intrinsically derive nonlinear classifiers. The idea is to use a kernel function which maps the original nonlinearly separable problem to a space of intrinsically larger dimensionality where the classes are linearly separable. A major problem in the design of kernel methods is to find the kernel parameters that make the problem linear in the mapped representation. This paper derives the first criterion that specifically aims to find a kernel representation where the Bayes classifier becomes linear. We illustrate how this result can be successfully applied in several kernel discriminant analysis algorithms. Experimental results using a large number of databases and classifiers demonstrate the utility of the proposed approach. The paper also shows (theoretically and experimentally) that a kernel version of Subclass Discriminant Analysis yields the highest recognition rates.


Kernel - Operating Systems - Linux - Function - Ubuntu

We propose a convex formulation for silhouette and stereo fusion in 3D reconstruction from multiple images. The key idea is to show that the reconstruction problem can be cast as one of minimizing a convex functional where the exact silhouette consistency is imposed as convex constraints that restrict the domain of feasible functions. As a consequence, we can retain the original stereo-weighted surface area as a cost functional without heuristic modifications of this energy by balloon terms or other strategies, yet still obtain meaningful (nonempty) reconstructions which are guaranteed to be silhouette-consistent. We prove that the proposed convex relaxation approach provides solutions that lie within a bound of the optimal solution. Compared to existing alternatives, the proposed method does not depend on initialization and leads to a simpler and more robust numerical scheme for imposing silhouette consistency obtained by projection onto convex sets. We show that this projection can be solved exactly using an efficient algorithm. We propose a parallel implementation of the resulting convex optimization problem on a graphics card. We are able to compute highly accurate and silhouette-consistent reconstructions for challenging real-world data sets. In particular, experimental results demonstrate that the proposed silhouette constraints help to preserve fine-scale details of the reconstructed shape.


Optimization - Algorithm - Function - Programming - Languages