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Autonomous field navigation, data acquisition and node location in wireless sensor networks

机译:无线传感器网络中的自动领域导航,数据采集和节点位置

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To overcome the limited transmission range of spatially separated nodes of a wireless sensor network (WSN), a small 4-wheel autonomous robot assembled the data from nodes distributed in a vineyard. First, the robot followed a predefined way-point route between the grapevine rows, in order to evaluate the sensor node locations by their received signal strength indication (RSSI). Then, the recorded and geo-referenced RSSI data were analysed and mapped. By using the evaluated node positions, an optimised second route was generated. While navigating, a laser scanner was used for obstacle detection and avoidance. Path planning with known positions of the nodes reduced the driving time by 15 times compared with the first run, because the hybrid control system used was capable of navigating within the plantation even perpendicular to the row structures. For locating the nodes, results based on trilateration were compared with the values of an attached differential global navigation satellite system (DGNSS). The results showed that it is possible to locate and geo-reference the sensor nodes with a robot, even without any prior knowledge about their absolute position. The best achieved location showed a deviation with DGNSS of 1.2 m and with RSSI trilateration of 0.6 m compared to the actual position.
机译:为了克服无线传感器网络(WSN)中空间分离节点的有限传输范围,一个小型四轮自主机器人从分布在葡萄园中的节点组装数据。首先,机器人遵循葡萄藤行之间预定义的路径点路线,以便根据传感器节点的接收信号强度指示(RSSI)评估其位置。然后,对记录的和地理参考的RSSI数据进行分析并绘制地图。通过使用评估的节点位置,生成优化的第二条路由。导航时,使用激光扫描仪进行障碍物检测和回避。与第一次运行相比,已知节点位置的路径规划减少了15倍的驾驶时间,因为使用的混合控制系统能够在种植园内导航,甚至垂直于行结构。为了定位节点,将基于三边测量的结果与所附差分全球导航卫星系统(DGNSS)的值进行了比较。结果表明,即使事先不知道传感器节点的绝对位置,也可以用机器人定位和地理参考传感器节点。与实际位置相比,最佳位置的DGNSS偏差为1.2m,RSSI三边测量偏差为0.6m。

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