Image courtesy NASA/JPL-Caltech/MSSS
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Moving from point A to point B sounds so easy. We humans do it all day, every day. For a robot, though, navigation -- especially through a single environment that changes constantly or among environments it's never encountered before -- can be tricky business. First, the robot must be able to perceive its environment, and then it must be able to make sense of the incoming data.
Roboticists address the first issue by arming their machines with an array of sensors, scanners, cameras and other high-tech tools to assess their surroundings. Laser scanners have become increasingly popular, although they can't be used in aquatic environments because water tends to disrupt the light and dramatically reduces the sensor's range. Sonar technology offers a viable option in underwater robots, but in land-based applications, it's far less accurate. And, of course, a vision system consisting of a set of integrated stereoscopic cameras can help a robot to "see" its landscape.
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Collecting data about the environment is only half the battle. The bigger challenge involves processing that data and using it to make decisions. Many researchers have their robots navigate by using a prespecified map or constructing a map on the fly. In robotics, this is known as SLAM -- simultaneous localization and mapping. Mapping describes how a robot converts information gathered with its sensors into a given representation. Localization describes how a robot positions itself relative to the map. In practice, these two processes must occur simultaneously, creating a chicken-and-egg conundrum that researchers have been able to overcome with more powerful computers and advanced algorithms that calculate position based on probabilities.
JP Buntinx is a FinTech and Bitcoin enthusiast living in Belgium. His passion for finance and technology made him one of the world's leading freelance Bitcoin writers, and he aims to achieve the same level of respect in the FinTech sector.
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