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A new form of propulsion that could allow microrobots to explore human bodies has been discovered. The technique would be used to power robots and other devices such as microfluidic pumps from a distance. Finding a propulsion mechanism that works on the microscopic scale is one of the key challenges for developing microrobots. Another is to find a way to supply such a device with energy because there is so little room to carry on-board fuel or batteries. Now a team lead by Orlin Velev at North Carolina State University in Raleigh, US, has found that a simple electronic diode could overcome both these problems. Velev and Vesselin Paunov from the University of Hull, UK, floated a diode in a tank of salt water and zapped the set-up with an alternating electric field.

A new form of propulsion that could allow microrobots to explore human bodies has been discovered. The technique would be used to power robots and other devices such as microfluidic pumps from a distance. Finding a propulsion mechanism that works on the microscopic scale is one of the key challenges for developing microrobots. Another is to find a way to supply such a device with energy because there is so little room to carry on-board fuel or batteries. Now a team lead by Orlin Velev at North Carolina State University in Raleigh, US, has found that a simple electronic diode could overcome both these problems. Velev and Vesselin Paunov from the University of Hull, UK, floated a diode in a tank of salt water and zapped the set-up with an alternating electric field.

National Instruments has released a vision module for the PXI platform that provides a high-performance parallel processing architecture for hardware-defined timing, control and image pre-processing. The NI 1483 Camera Link adapter module, in combination with an NI FlexRIO field-programmable gate array (FPGA) board, offers a solution for embedding vision and control algorithms directly on FPGAs which are used to process and analyse an image in real time with little to no CPU intervention. The FPGAs can be used to perform operations by pixel, line and region of interest. They can implement many image processing algorithms that are inherently parallel, including fast Fourier transforms (FFTs), thresholding and filtering.

Field programmable gate arrays (FPGAs) present an efficient and inexpensive alternative when it comes to implementing complete embedded systems along with important peripheral functions. The reconfigurable logic circuitry of an FPGA offers tremendous flexibility. A lesser known feature is that the outputs of a digital FPGA also permit various analogue applications.

Recent advances in the size and performance of FPGAs, coupled with advantages in time-to-market, field-reconfigurability and lower up-front costs, make FPGAs ideally suited to a wide range of commercial and defense applications [6]. In addition, FPGAs generality and reconfigurability provide important protections against the introduction of Trojan horses during semiconductor manufacturing process[8]. As a result, FPGA applications increasingly involve highly-sensitive intellectual property and trade-secrets, as well as cryptographic keys and algorithms [7].

It is no secret that field-programmable gate arrays (FPGAs) are getting bigger and more complex all the time. The fabrication process creates smaller transistors and makes more dense chips packing more digital processing per nanometer. Engineers love to see advancement because it means they can do more with modern silicon, and many times NI LabVIEW FPGA Module technology helps by abstracting the complexity to a higher level so that engineers can more smoothly take advantage of these improvements.  Unfortunately, there is one issue with FPGAs that continues to be a time sink and only gets worse with denser FPGAs: compilation time.

Within the next three years, the U.S. military will test the feasibility of sending a quadruped robot out into the field as a trusty pack mule to carry supplies for its troops, wherever they go. If the testing goes well for Boston Dynamics's Legged Squad Support System (LS3), company founder Marc Raibert will have come a long way from the one-legged hopping robots he pioneered in the 1980s. Actually Raibert has already come a long way, to the point where the U.S. Defense Advanced Research Projects Agency's (DARPA) Tactical Technology Office and the U.S. Marine Corps awarded his company a 30-month, $32-million contract last week to deliver a prototype LS3. This would be the first step in fulfilling the military's call for an autonomous, legged robot that can carry up to 181 kilograms of supplies for at least 32 kilometers without refueling.

Have you ever written code that behaves correctly under a simulator only to have intermittent failures in the field? Or maybe your code no longer functions properly when you compile with a newer version of your tool chain. You review your test bench and verify 100 percent complete test coverage and that all tests have passed with no errors--yet the problem stubbornly remains. While designers understandably place great emphasis on coding and simulation, they often have only a nodding acquaintance with the internal workings of the silicon within an FPGA. As a result, incorrect logic synthesis and timing problems, rather than logic errors, are the cause of most logic failures. But writing FPGA code that creates predictable, reliable logic is simple if designers take the right steps. In FPGA design, logic synthesis and related timing closure occur during compilation. And many things, including I/O cell structure, asynchronous logic and timing constraints, can have a big impact on the compilation process, varying results with each pass through the tool chain. Let's take a closer look at ways to eliminate these variances to better and more quickly achieve timing closure.

With all of the new competition in the consumer robotics field, it’s about time for iRobot to show that they’re still capable of innovating new and exciting things. AVA, their technology demonstrator, definitely fits into the new and exciting category. AVA is short for ‘Avatar,’ although iRobot was careful not to call it a telepresence robot so as not to restrict perceptions of what it’s capable of. AVA is capable of fully autonomous navigation, relying on a Kinect-style depth sensing camera, laser rangefinders, inertial movement sensors, ultrasonic sensors, and (as a last resort) bump sensors. We got a run-down a few days ago at CES, check it out: