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Autonomous Surface Vehicle(ASV)Obstacle Avoidance Using Fuzzy Kohonen Network(FKN)

机译:自动表面车辆(ASV)使用模糊kohonen网络(FKN)避免避免

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Autonomous Surface Vehicle(ASV)is robot boats which can navigate autonomously to avoid obstacles in their path direction.The ASV has designed to detect and measure the distance of the position of the obstacle.The Fuzzy Kohonen Network(FKN)method is applying to the ASV as its brain to divine the manoeuvre what should do.The FKN is getting the information(Crips)from two sonar sensors,where are located in front of the ASV.In this experiment the FKN has four(4)pattern scenario which is three pattern normal condition,and one pattern danger condition.The three normal conditions define as if no obstacle detecting by two sensors,or either one of the sensor has detected an obstacle.The last condition pattern(danger)will occur when each sensor has detected the obstacle.These pattern condition is coding in C# and embedded into ATMega328.The range sensor is setting for 50-210 cm with the error rate is less than 1%.The manoeuvre of this ASV is providing two DC motors by controlling the PWM value.As the results of the experiment,The manoeuvre is quicker and smooth without a crash to obstacles.
机译:自主地面车辆(ASV)是机器人船,可以自主导航以避免路径方向上​​的障碍物。ASV设计用于检测和测量障碍物位置的距离。模糊的科霍恩网络(FKN)方法施加到ASV作为其大脑将动作除外.FKN是从两个声纳传感器获取信息(累积),位于ASV的前面。在此实验中,FKN具有四(4)个模式场景,这是三(4)个模式场景模式正常情况,以及一个模式危险条件。三个正常条件定义好像没有两个传感器检测到障碍物,或者传感器中的任何一个都检测到障碍物。当每个传感器检测到时,将发生最后一个条件模式(危险)这些模式条件是在C#中编码并嵌入到ATMEGA328中。范围传感器设置为50-210厘米,错误率小于1%。该ASV的机动通过控制PWM值提供两个直流电机。作为实验结果,机动更快,流畅,没有碰撞到障碍。

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