Part of the decoding process includes having a robot sweep over an object and collect EF information from it, Smith explains. The algorithm then compares, in near real time, this data to a series of prerecorded signals that describe various shapes, sizes, and orientations of the object. When the algorithm finds a reasonably certain match, it adjusts the robotic fingers so that they can grasp the object.
EF sensing isn’t the only form of sensing that robots use. Often, a machine will use a video camera to detect objects at a long range. And robotic cars, such as those built for the Urban and Grand Challenges, sponsored by the Defense Advanced Research Projects Agency, use laser range finders that shine an infrared beam onto objects and use the reflected light to build maps of their environment. Both options are relatively expensive, and video, in particular, becomes limited at close range as a robot’s hand covers an object.
“One of the major problems in robotics has to do with the ability of a robot to interact and touch and feel and manipulate an object,” says Oussama Khatib, a professor of computer science at Stanford, in Palo Alto, CA. Khatib says that while Intel’s research looks like a promising approach to close-proximity sensing, it still needs to be integrated more completely in robots. “This is something that is important and significant if we can prove its robustness and its ability to be integrated with robotic systems and human environment in an effective way,” he says. Khatib adds that future proximity-sensing robots will most likely have a number of sensors that measure different aspects of their environment, which will require algorithms that can integrate all the disparate signals.
Smith agrees that ultimately, proximity sensing will rely on numerous sensors. EF sensing has its limits: it can’t see insulating objects such as thin plastic, thin pieces of wood, and paper. (As insulating objects become thicker, they become more perceivable.) Smith and his team are exploring other sensors, such as those that measure the reflection of light. But, he says, in many instances, EF sensors have advantages over optical sensors: they are less affected by different textures, and the data usually has fewer random fluctuations or noise.
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