The famous Moravik paradox in the field of artificial intelligence and robotics says that, unlike traditional assumptions, for computers, the realization of advanced intelligence such as logical reasoning requires relatively little computing power, and achieves low-level intelligence such as perception and movement. It requires huge computing resources.
AlphaGo, the artificial intelligence program of Google’s GoogleDeepMind, has been crushed on the “human final intellectual prideâ€. Even a small piece of chess needs human help to complete it. It is a powerful proof of Moravik’s paradox that the computer is It is relatively easy to show an adult's level in an intelligence test or in chess, but it is quite difficult to make a computer feel like a one-year-old child.
In robot systems, autonomous navigation is a core technology that is the key to giving robots the ability to sense and act. Let's take a look at the four navigation and positioning methods commonly used by autonomous mobile robots.
1, visual navigation and positioning
In the visual navigation and positioning system, the current application at home and abroad is based on local vision, the navigation method of installing the vehicle camera in the robot. In this navigation mode, the control device and the sensing device are loaded on the robot body, and high-level decisions such as image recognition and path planning are all completed by the vehicle control computer.
The visual navigation positioning system mainly includes: a camera (or CCD image sensor), a video signal digitizing device, a DSP-based fast signal processor, a computer and peripherals thereof. There are many robot systems that use CCD image sensors. The basic component is a row of silicon imaging elements. Photosensors and charge transfer devices are arranged on a substrate. The video signals of multiple pixels are time-sharing and sequential by the sequential transfer of charges. The image taken by the area CCD sensor can be taken from 32×32 to 1024×1024 pixels.
The working principle of the visual navigation and positioning system is simply to optically process the environment around the robot. The camera first collects the image information, compresses the collected information, and then feeds it back to a neural network and statistical methods. The learning subsystem, and then the learning subsystem associates the acquired image information with the actual position of the robot to complete the autonomous navigation and positioning function of the robot.
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