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Recognition of plants with CTFM ultrasonic range data using a neural network

机译:使用神经网络识别具有CTFM超声波测距数据的植物

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The success of any mobile robot is dependant on its ability to perceive the environment in which it is working. This paper describes some work to verify the suitability of a new sensor as a landmark detector for a mobile robot. The landmarks that we are investigating are plants. An ultrasonic sensor continually transmits a frequency modulated signal. Echoes detected by a second transducer are demodulated with the transmitted signal to produce audio tones which are proportional to range. The spectrum of these tones is determined by the geometry of the object. An artificial neural network (ANN) is used to recognise plants. This paper discusses a series of experiments that prove that the machine perception system is independent of the range, size, and the orientation of the plant. This system has potential applications in industrial and office robots.
机译:任何移动机器人的成功取决于其感知其工作环境的能力。本文介绍了一些工作,以验证新传感器是否适合作为移动机器人的界标检测器。我们正在调查的地标是植物。超声波传感器连续发送调频信号。由第二换能器检测到的回声将与传输的信号一起解调,以产生与范围成比例的音频音调。这些音调的频谱由物体的几何形状确定。人工神经网络(ANN)用于识别植物。本文讨论了一系列实验,这些实验证明了机器感知系统与植物的范围,大小和方向无关。该系统在工业和办公机器人中具有潜在的应用。

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