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首页> 外文会议>2011 IEEE International Conference on Systems, Man, and Cybernetics >Adaptive robot navigation protocol for estimating variable terrain elevation data
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Adaptive robot navigation protocol for estimating variable terrain elevation data

机译：自适应机器人导航协议，用于估算可变的地形高程数据

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摘要

Efficiently measuring environmental phenomena (e.g., elevation, chemical composition, and mineral density) is a task typically reserved for the geoscience community. Recent robotic systems with the potential for addressing the task of sampling currently exist, yet their navigation strategies (and subsequently sampling strategies) are seldom a function of the spatial change in the measured phenomena of interest. Solutions are especially void for intelligent systems to which resource constraints are applied (i.e., battery power and experimentation time) while complete coverage of an area is expected. In this paper, we discuss the implementation of a custom navigation strategy based on immediately-sensed data that, when combined with spatial interpolation techniques, yields a re-creation of the surveyed space with root mean squared error that meets accepted mapping standards. Our methodology employs an adaptive coverage algorithm which succeeds in lowering the RMS error when compared to other navigation techniques. Our results are validated in simulation by considering: 1) randomly-generated terrains and 2) realistic digital elevation map (DEM) data transposed from publically available terrain contour maps.
机译：有效测量环境现象（例如海拔，化学成分和矿物质密度）是地球科学界通常要做的任务。当前存在具有解决采样任务潜力的最新机器人系统，但是它们的导航策略（以及随后的采样策略）很少是所测量的感兴趣现象的空间变化的函数。对于应用了资源限制（即电池电量和实验时间）的智能系统而言，解决方案尤其无效，而预计该区域会完全覆盖。在本文中，我们讨论了基于即时感知数据的自定义导航策略的实现，当与空间插值技术结合使用时，可以重新生成所调查空间的均方根误差，且该误差均符合公认的制图标准。我们的方法采用了自适应覆盖算法，与其他导航技术相比，该算法成功地降低了RMS误差。通过考虑以下因素，我们的结果在仿真中得到了验证：1）随机生成的地形; 2）从公开可用的地形等高线图转换而来的真实数字高程图（DEM）数据。

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